K-means

Image Databases and archives provide lot of research areas. Significant among them is ; The Contentbased image retrieval (CBIR) research area for manipulating large amount of image databases and archives. CBIR is mainly based on the way that the image is extracted. The main focus of the proposed system is on the color and shape feature extractions for hand tracking that is intended as a step towards palm and face tracking for a perceptual user interface. This paper extends a default implementation to allow tracking on type of feature spaces and arbitrary number by reviewing the k-means clustering algorithm. We weigh the multidimensional histogram with a simple monotonically decreasing kernel profile prior to histogram back projection in order to compute the new portability that a pixel value belongs to the target model. We examine the effectiveness of the K-means clustering algorithm as a general-purpose hand and face tracking approach in the case where no assumptions have been made about the palm to tracked in this paper image retrieval

This research intends to detect the infected and healthy coconut trees by their images. Because coconut trees are rummage-sale for multiuse perpetual crop of the tropics. Each and every parts of coconut tree are used in at any rate. So identification of infected tree is more vibrant for commercial purpose. Initially researcher collects the images of coconut tree. Then Gray-Level Co-Occurrence Matrix (GLCM) features are extracted from the images and K-means clustering is applied for segmenting the infecting parts of the images. Then the features are feed to classification algorithms such as Neural Networks and Support Vector Machine to classify the images into infected and healthy and achieved classification accuracy 85% and 89% respectively.

K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset

To download the dataset, go to:
http://attributes.kyb.tuebingen.mpg.de/AwA-base.tar.bz2

This Document is also available in ipython notebook format at:
https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/K-Means%2C%20PCA%2C%20and%20Dendrogram%20on%20the%20Animals%20with%20Attributes%20Dataset.ipynb

About the dataset: This is a small dataset that has information on about 50 animals. The animals are listed in classes.txt. For each animal, the information consists of values for 85 features: does the animal have a tail, is it slow, does it have tusks, etc. The details of the features are in the predicates.txt. The full data consists of a 50 x 85 matrix of real values, in predicate-matrix-continuous.txt. There is also a binarized version of this data, in predicate-matrix-binary.txt.

This document goes over K-Means, PCA, and Hierarchical Clustering of the Animals with Attributes Dataset.

Cyber crime is a worldwide problem, with a global reach. Cyber crimes do not respect national boundaries, and so can be sent to and from anywhere in the world. Many reports, mostly by cyber security firms, regularly release information ranking the different nations in terms of top cyber crime output, broken down into varying cyber crime types. However, little has been done to classify nations according to separate cyber crime typologies using any multivariate methods. Instead, reporting is descriptive and unidimensional. The present research sought to fill this gap by conducting K-means clustering analysis on a sample of 190 countries using seven dimensions of cyber crime ranging from malware, fraud, spam, and digital piracy, as well as measures of GDP and internet use. The findings determined that nations can be broken down into four distinct categories based on cyber crime activity: low cyber crime countries, non-serious cyber crime countries, advance fee fraud countries, and phishing scam countries. The implications of these findings and the directions for future research are discussed. ________________________________________________________________________

Candi merupakan tempat bersejarah, karena merupakan peninggalan purbakala dari kerajaan Hindu-Buddha. Candi juga digunakan sebagai tempat peribadatan umat Hindu-Buddha. Candi Jago memiliki arsitektur bangunan yang bernilai seni tinggi, karena hanya pada peradaban yang cukup makmur dan terpenuhi kebutuhan sandang pangannya saja yang mampu menciptakannya. Segmentasi sangat penting dalam pengolahan citra sehingga sangat penting untuk dikembangkan, karena hasil segmentasi citra asli akan berpengaruh pada proses berikutnya, seperti pengenalan pola karakter, klasifikasi penyakit berdasarkan citra serta pengenalan wajah. Teknik yang populer digunakan dalam segmentasi citra adalah menggunakan metode K-Means.          K-Means adalah algoritma berbasis pengelompokan (clustering), dimana K-Means melakukan clustering  data dengan menentukan titik pusat (centroid) sebagai acuan. Pada penelitian ini akan diterapkan segmentasi citra dengan metode clustering K-Means. Input citra yang diambil berupa gambar berwarna dari gambar Candi Jago, kemudian data akan dikelompokkan  berdasarkan piksel-piksel dari citra yang memiliki karakteristik sama. Satu region atau daerah terdiri dari piksel-piksel yang memiliki karakteristik sama sehingga akan menghasilkan citra yang tersegmentasi berdasarkan jumlah clustering yang diinginkan.

Wireless Sensor Networks (WSNs) are utilized for a plethora of applications such as weather forecasting, monitoring systems, surveillance, and so on. The critical issues of the WSN are energy constraints, limited memory, and computation time. This spectrum of criticality takes a deep dive with large-scale WSNs. In such scenario, the network lifetime has to be efficiently utilized with the available resources by organizing into clusters. Even though the technique of clustering has proven to be highly effective in minimizing the energy, the tradition cluster based WSNs, the protocol overhead is high for Cluster Heads (CHs) as it receives and aggregates the data from its cluster members. Therefore, efficient management of CH along with routing behavior is vital in prolonging the network lifetime. In this paper, an enhanced CH-Management technique is proposed which efficiently elects its CH using Particle Swarm Optimization (PSO), hereafter referred to as PSO_DDE. The PSO_DDE approach considers various parameters such as within-cluster distance between nodes (intra-cluster distance), neighbor density, and residual energy of nodes for the best candidate selection of CH. Also, the cluster formation is defined by the k-means based on the Euclidian distance. The PSO_DDE approach is integrated with the Dynamic Source Routing (DSR) for efficiently traversing the data packet to the sink node. The performance metrics are compared with the existing approaches using NS-2 simulator, and the proposed approach shows superiority of results.

It is important to reveal the relationship between the internal migration and the unemployment rate in the settlements that are experiencing this immigration in order to understand the causes and consequences of unemployment in these areas. But it needs a separate analyse to show direction of this relationship for the both migrated and emigrated regions. For this understanding, both immigration and unemployment statistics should be examined on the basis of settlements. In this study, we made a comparative analysis of cities in Turkey in terms of mobility of people among cities and unemployment rates in these areas and touched on the discussions in the literature. The main purpose of the study is to cluster the provinces in terms of unemployment and migration by using k-means algorithm as a data mining technique and to compare sets obtained. Unemployment and migration statistics of TÜİK (Turkish Statistical Institute) were used in the analysis. For model performance of the k-means algorithm, in each trial, the numbers of clusters were determined in order to ensure minimum distance for intracluster and maximum distances for between the clusters. Analyses were performed on RStudio with the R Programming Language and findings were made more attractive and understandable by visualizing.

One of the challenges in medical science is to detect and identify diseases for early detection by medical imaging technology. Medical imaging modalities such as X- Ray, Computed Tomography (CT) Scan, Magnetic Resonance Imaging (MRI), and Ultrasound produce medical image captured from human body arrangement for analysis and diagnosis.  One of the technologies used to solve the problem is by image processing.  However, this method required knowledge and ability to apply many different methods in an analysis.

Customer churn is a significant issue that is regularly related with the existence cycle of the business. At the point when the business is in a development period of its life cycle, deals are expanding exponentially and the quantity of new clients to a great extent dwarfs the quantity of churners. On the other side, organizations in a develop period of in their life cycle, set their attention on lessening the rate of customer churn. This research work proposes an efficient computational intelligence model comprising of clustering achieved through improvised K-Means algorithm and classification achieved through Non Linear Support Vector Machine.

El presente proyecto busca analizar el comportamiento de los hurtos a personas que afectan la localidad Los Mártires de la ciudad de Bogotá, haciendo uso de algoritmos de agrupamiento de minería de datos espaciales y apoyado en una infraestructura de datos espacial, teniendo en cuenta variables como cuadrante de policía, mes, día del mes, día de la semana, hora, lugar de ocurrencia del delito, sexo y edad de la víctima; de tal forma que se logre realizar diferentes tipos de mapas del delito, y permita a las autoridades implementar estrategias para predecir el modo que opera, como se mueve, como se financia y lograr combatir con mayor eficacia y eficiencia el delito.
A lo largo del proyecto, se trabajaron los algoritmos de agrupamiento de minería de datos espaciales K-Means y DBSCAN, el primero se usó para establecer la ubicación óptima de cada estación de policía teniendo en cuenta la ubicación y distribución de los delitos, y el segundo para hallar las zonas con mayor actividad delincuencial. Estos dos en conjunto, sobre la Infraestructura de Datos Espaciales de Bogotá nos permitió construir los modelos y mapas temáticos del delito de la localidad Los Mártires, muchos de estos modelos fueron realizados filtrando la información por diferentes variables para tener un análisis más preciso.
Se trabajó con software libre para la elaboración de este proyecto.

Urban communities' behavior in disposing and managing waste around the house is still very concerning. This study aims to analyze and classify community behavior patterns related to waste disposal to help the Makassar City Government overcome waste problems. The methods and techniques applied are using the Waterfall model and the K-Means Algorithm. The stages of analysis and classification of behavior patterns can assist city governments in making strategic decisions. The variables determined include education, age, occupation, free time, smoking status, and questionnaire (outreach, law, knowledge, and facilities). The K-Means algorithm calculation results are 39 people in cluster 1 and 10 people in cluster 2. In cluster 1, it is known that the community tends to take care of the environment in the sub-district where the community lives. Cluster 2 has a low average yield, such as people who do not know the impact laws, inadequate sanitation facilities, and lack of proper waste management towards smart city governance. Based on testing for each method, the Cyclomatic Complexity value generated is four. Therefore, it can be concluded that the white box testing on the K-Means algorithm runs well because each test produces the same value. The government and the community have a responsibility to carry out waste management properly, make use of goods and refill facilities, know the legal impact of littering and follow the socialization held by the Makassar City Government.

Clustering techniques are important to analyze the data due to the heavy increase of data in modern investigation. In real world these techniques are mostly used in many domains, some of them are financial analysis, social networks, digital marketing, etc. To support clustering over large scale datasets, public cloud infrastructure will play the major role for presentation of the data and financial trades. In our work, MapReduce based K-means++ clustering technique is proposed to allow the better grouping of data into appropriate clusters. The results of the paper denote that the present algorithm can efficiently process large amount of data.

At Rafadel Store Acc various types of accessories available that are sold in the Store. Of the various types of accessories are sold certainly not all of them sold and also there are less salable and some are never sold at all. With this problem then we need to do calculations to determine or classify which categories of accessories that sell, sell and sell unsold, in the process of grouping it will be used a method of clustering using K-Means Clustering Algorithm as a method of calculation manually and in the implementation then used a Data Mining software using RapidMiner Studio version 8.2. With the RapidMiner Studio application store owners can see the results of the grouping which accessories are the most salable, sellable and less salable. So, if there are products that do not sell, shop owners can look for other alternatives to unsold accessories that can be sold. The method used in data collection is observation and interview to Rafadel Acc shop owner. Abstrak: Pada toko Rafadel Acc menjual berbagai jenis aksesoris yang tersedia yang dijual di toko tersebut. Dari berbagai jenis aksesoris yang dijual tentu tidak semuanya yang laku terjual dan juga ada yang kurang laku serta ada juga yang tidak pernah terjual sama sekali. Dengan adanya masalah ini maka kita perlu melakukan perhitungan untuk menentukan atau mengelompokkan mana kategori aksesoris yang laku, kurang laku dan tidak laku terjual, dalam proses pengelompokan maka akan digunakan sebuah metode pengelompokan menggunakan Algoritma K-Means Clustering sebagai metode perhitungan secara manual dan dalam implementasinya maka digunakan sebuah software Data Mining menggunakan RapidMiner Studio versi 8.2. Dengan adanya aplikasi RapidMiner Studio ini pemilik toko dapat melihat hasil pengelompokan aksesoris mana yang paling laku, laku dan kurang laku. Maka, bila terdapat produk yang tidak laku, pemilik toko dapat mencari alternative lain agar aksesoris yang tidak laku dapat menjadi laku. Metode yang digunakan dalam pengumpulan data adalah observasi dan wawancara kepada pemilik toko Rafadel Acc.

in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it's important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.

Today is a knowledge age so that world needs to become a more richer palace for everyone. Students can
learn their lectures and students can do their exercises on the web as individually or collaboratively with
their peers like directed by the teacher by using the think-pair-share technique. The system provides the
ability to clear to decide on their choices about the questions. The K-means clustering method is used to
modify the pair state and support for determining students’ grade of classes. The main objective of this
study is to design a model for java programming learning system that facilitates the collaborative learning
activities in a virtual classroom.

یکی از مشکلات بزرگ نظام آموزش و پرورش و خانوادههای دارای فرزند که هر سال منجر به هدر رفتن میزان زیادی از امکانات، منابع و
استعدادهای بالقوه انسانی میشود و تأثیرات غیرقابل جبرانی را در ابعاد فردی و اجتماعی به جای میگذارد، پدیدهی افت تحصیلی است. بنابراین
نظام آموزش و خانوادهها باید به دنبال راه حلی برای به حداقل رساندن این خسارتها باشند. این مقاله با شناسایی عوامل موثر بر افت تحصیلی با
استفاده از دادهکاوی آموزشی، تلاش میکند تا با معرفی این عوامل مشکلساز به مسوولین آموزشی از وقوع این پدیده جلوگیری شود. در این
به اطلاعات جمعآوری شده از دانش آموزان سال اول یکی CART و درخت تصمیم K-Means پژوهش پس از اعمال الگوریتمهای خوشه بندی
از دبیرستانهای شهر یزد، مهمترین عوامل موثر بر وقوع افت تحصیلی استخراج شدند.

This research provides us a model to cluster Multi-variant databases, using Factor Analysis with Principle Component Analysis (PCA) in reducing the Dimensions through deriving collection of factors from all variables, then using K-Means Algorithm in apply Clustering Data which it’s one of the most famous Clustering Algorithm. We compared the results of Clustering Algorithm before and after applying the PCA on the Database by using Total Sum of errors squares (SSE) for the resultant clusters as evaluation criteria. The applied result of this model on a virtual database which is specialized in insurance and lending showed a clearly improvement in clients clustering, this database contains more than one type of variables and many objects. Finally, we demonstrated the factors and variables significant on the execution of clustering algorithm.

The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining interesting research areas. Data mining tools and techniques help to discover and understand hidden patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate clustering method and optimal number of clusters in healthcare data can be confusing and difficult most times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms. This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (K-means and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.

This research presents a model to collect the necessary data on the books through the reference setting (Bibliography) and analysis of the book technically to make the information provided for the researcher is scientific and well planned relying on Market Segmentation principle to know the customer characteristics, and personalization relied on Implicit and Explicit for the collection of data by Tracking customer behavior models, thereby forming a data warehouse is useful in the future decision makers to generate knowledge and to take decisions on the electronic library of the university.to guide the researchers to their aim quickly
The researcher has modeled all of this data based on Object Oriented Modeling as one of modern approaches to Systems Analysis, and This research provides us a model to cluster Multi-variant databases, using Factor Analysis with Principle Component Analysis (PCA) in reducing the Dimensions through deriving collection of factors from all variables, then using one of the most famous Clustering Algorithm(K-Means) in apply Clustering and We compared the results of Clustering Algorithm before and after applying the PCA on the Database by using Total Sum of errors squares (SSE) for the resultant clusters as evaluation criteria this where is  database contains more than one type of variables and many objects  and we demonstrated the factors and variables significant on the execution of clustering algorithm and using nervous  network in order  to classify the customers and for forecasting of the groups which belong to it, and this model was applied through designing electronic library system in the Faculty of Economics - University of Aleppo.

Veritabanlarında Bilgi Keşfi olarak da adlandırılan Veri Madenciliği, veritabanları ve veri ambarları gibi çeşitli veri depolarında saklanmakta olan büyük miktardaki verinin işlenerek içindeki geçerli, daha önceden bilinmeyen, potansiyel olarak kullanışlı, yararlı ve değerli olabilecek bilginin çıkartılması sürecidir. Veri madenciliğinin önemli tekniklerinden biri olan veri kümeleme, benzer veri nesnelerinin farklı gruplara sınıflandırılması işlemidir. Bir veri kümesi belirli bir uzaklık ölçütüne göre alt kümelere ayrılırken hedef her bir altkümedeki nesnelerin ortak bir karaktere sahip olmasıdır. Metin Kümeleme ise doküman koleksiyonlarının doküman benzerliklerine bağlı olarak kümelere ayrıştırılmasıdır. Sonuç olarak, bir küme içerisindeki dokümanların genellikle benzer bir konuda olmaları beklenir.

Bu tez çalışmasında, metin veri setlerinin kümelenmesi için geliştirilmiş algoritma ve yaklaşımlar ayrıntılı olarak incelenmiş, çok boyutlu ve çok büyük metin veri setlerini kümelenmesinde karşılaşılan sorunlar ve zorluklar irdelenerek bunlar için çözüm yöntemleri getirilmiştir.

Tez çalışması dört bölümden oluşmaktadır. İlk bölümde öncelikle veri madenciliği, metin madenciliği ve metin kümeleme kavramları tanımlanarak metin kümelemedeki güncel problemlerden bahsedilmiştir. Daha sonra, tez kapsamında çözüm getirilen problemin tanımı yapılarak tezin amacı ve organizasyonu verilmiştir.

Problemin tanımının ardından ikinci bölümde metin madenciliği sistemlerinin genel yapısı, metin kümeleme, metin kümelemede karşılaşılan sorunlar, önişleme teknikleri, doküman temsil yöntemleri, boyut indirgeme teknikleri incelenmiştir. Ayrıca metin kümeleme sürecinin kalitesini ve başarımını doğrudan etkileyen uzaklık ve benzerlik ölçüm yöntemleri ile kümeleme kalitesi ölçüm yöntemleri ele alınmıştır. Bu bölümde son olarak metin kümeleme alanında yapılmış önemli çalışmaların, önerdikleri yaklaşım ve çözüm yöntemleriyle kapsamlı olarak incelendiği geniş bir literatür taramasına yer verilmiştir.

Tez kapsamında, çok boyutlu doküman koleksiyonlarının yüksek başarımla ve verimli bir şekilde kümelenebilmesi amacıyla, keskin kümeleme yapan Küresel K-Means algoritmasında değişiklik yapılarak esnek kümeleme yaklaşımlarındaki örtüşen kümeler oluşabilmesi fikri Küresel K-Means algoritmasına uygulanmış, K-Means döngüsü içerisinde dokümanların kümelere benzerliklerine göre belli ölçüde birden çok kümeye dahil olmasına izin verildiği özgün bir algoritma olan Çoklu-Küme Küresel K-Means algoritması geliştirilmiştir. Üçüncü bölümde, geliştirilen bu yeni kümeleme algoritmasının yapısı ayrıntılı olarak açıklanmış ve analiz edilmiştir. Çeşitli metin veri setleri üzerinde gerçekleştirilen deneylerle Çoklu-Küme Küresel K-Means algoritmasının işlemci zamanı kullanımında hissedilir bir fark oluşturmadan kümeleme kalitesinde büyük bir artış sağladığı, temel alınan Küresel K-Means algoritmasının ölçeklenebilirliğini koruyarak büyük doküman koleksiyonları üzerinde uygulanabilir olduğu gösterilmiştir.

Dördüncü bölümde, tez çalışması ile geliştirilen algoritmanın genel değerlendirmesi yapılarak sağlanan bilimsel katkılar özetlenmiş, konu ile ilgili çalışacak araştırmacılar için öneriler verilmiştir.

В статье проведен анализ научной литературы, посвященной кластерному анализу методом К-средних, а также рассмотрено применение метода в психологических исследованиях украинских ученых. Показано, что большинство отечественных публикаций содержат недостаточное описание процедуры применения метода К-средних, что может привести к невоспроизводимости результатов исследований. Детально описаны этапы применения метода и решения, которые необходимо принимать в связи с его использованием в исследовательской практике. Даны рекомендации по улучшению качества дальнейших научных работ с применением кластерного анализа методом К-средних.

( Alternatif Link download Buku Analisis Big Data: http://bit.ly/2x8ta9S )

Alhamdulillahhi robbil alamin, puji syukur kehadirat Allah SWT atas segala rahmat dan karunia-Nya dengan terselesaikannya penulisan buku ini dengan judul “Analisis Big Data”. Buku ini merupakan uraian untuk memudahkan pemahaman konsep, tingkat dasar sampai lanjut dalam sistem cerdas dan penerapannya melalui pemanfaatan teknologi Big Data, dengan mengedepankan keterampilan dalam pembuatan dan hasil implementasi dengan berbagai kombinasi algoritma berbasis sistem cerdas maupun dengan perpaduan berbagai macam tools untuk membangun ekosistem analisis Big Data yang powerfull. Konsep sederhana Analisis Big Data mencakup Volume, Velocity, dan Variety (3V), dan pengembangannya ada yang menyebut (7V) termasuk Volume, Velocity, Variety, Variability, Veracity, Value dan Visualization, atau 10V bahkan lebih dari itu, tetapi sebaiknya tidak membatasi pemahaman Big Data ini hanya dengan sedikit banyaknya istilah dari huruf V yang digunakan :). Kemudian dengan adanya ekosistem tersebut, jika dibutuhkan analisis sederhana maupun yang lebih kompleks, maka harapannya tidak akan ada kendala dari besarnya data yang diolah. Adanya kemajuan teknologi dalam hal penyimpanan, pengolahan, dan analisis Big Data meliputi (a) penurunan secara cepat terhadap biaya penyimpanan data dalam beberapa tahun terakhir; (b) fleksibilitas dan efektivitas biaya pada pusat data dan komputasi awan untuk perhitungan dengan konsep elastisitas dan penyimpanannya; serta (c) pengembangan kerangka kerja baru seperti Hadoop ecosystem (salah satu peluang bisnis yang besar untuk developer untuk saat ini dan ke depannya dalam rangka membangun ekosistem analisis Big Data yang sangat powerfull sekelas Cloudera, Hortonworks, etc), yang memungkinkan pengguna untuk mengambil manfaat dari sistem komputasi terdistribusi, misal untuk menyimpan sejumlah data yang besar melalui pemrosesan parallel, dukungan database NoSQL, dan komputasi berbasis streaming. Sehingga kemajuan teknologi ini telah menciptakan beberapa perbedaan yang sangat signifikan, misal dalam hal kecepatan maupun ketepatan dari hasil yang didapatkan antara analisis tradisional dengan tools yang bukan dengan konsep Big Data versus analisis modern untuk saat ini dan masa depan dengan membangun ekosistem Big Data yang sangat powerfull.
Penulis mengucapkan terimakasih yang sebesar-besarnya kepada kedua orang tua penulis, spesial kepada Istriku tercinta “Alfun Lailatul Musabbikha”, bapak ibu mertua, dik Wiwik, dik Ganda, kak Mat, kak Deri, Bude Gi, Pakde Dul, Bude Gani, Pak Abu, mbak Nik, dik Andre, Mbak As, Mbak Lia, Mas Mualif, Mbak Lis, Pakde Naji, Bulik Yul, Pakde Tasrun, Pakde Zen, Pakde Zud, Paklik Fiq, mas Bowo dan semua sanak saudaraku yang tak bisa kusebutkan seluruhnya, serta beberapa pihak yang telah membantu penyelesaian buku ini:
1. Para penulis artikel Analisis Big Data di forum, web, blog dan buku yang menjadi referensi buku ini untuk memberikan masukan yang sangat berharga sekali untuk perbaikan dan penyelesaian buku ini.
2. Mbak Efi Riyandani, yang telah banyak membantu penulisan buku, dan mahasiswa-mahasiswa terbaik saya semester Ganjil 2016/2017, yaitu: Maryamah, Moh. Fadel Asikin, Daisy Kurniawaty, Selly Kurnia Sari, Nanda Agung Putra, Ardisa Tamara Putri, Dhimas Anjar Prabowo, Listiya Surtiningsih, Raissa Arniantya, Brillian Aristyo Rahadian, Diva Kurnianingtyas, Dyan Putri Mahardika, Tusty Nadia Maghfira.
3. Mahasiswa-mahasiswa terbaik saya semester Ganjil 2017/2018, yaitu: Yessica Inggir F., Kholifaul K., Ni Made Gita D. P., Ema Agasta, Retiana Fadma P. Sinaga, Fachrul Rozy Saputra Rangkuti, Yunita Dwi Alfiyanti, Dyah Ayu Wahyuning Dewi, Annisaa Amalia Safitri, Sarah Aditya Darmawan, Danastri Ramya Mehaninda, Eka Novita Shandra, Fakharuddin Farid Irfani, Rio Cahyo Anggono, Robih Dini, Yulia Kurniawati, Novirra Dwi Asri, Muhammad Vidi Mycharoka, Vania Nuraini Latifah, Olivia Bonita, Eka Miyahil Uyun, Cusen Mosabeth, Evilia Nur Harsanti, Ivarianti Sihaloho.

Semoga kontribusi kalian menjadi ilmu yang barokah dan bermanfaat. Aamiin. :). Tidak ada gading yang tak retak. Maka penulis memohon kritik dan saran untuk perbaikan dan penyempurnaan buku ini. In Syaa Allah pada edisi berikutnya, kami akan memberikan manualisasi Map Reduce, Spark, etc. dari setiap algoritma pada setiap contoh kasusnya. Selamat membaca buku ini dan semoga bermanfaat.

Malang, 19 Juli 2016-24 Mei 2018

Penulis

—The widespread application of data mining is highly noticeable fields like e-business, marketing and retail has led to its application in other industries and healthcare sectors. The healthcare environs are still information rich but that has poor knowledgeable data. Techniques in Data mining have been commonly used to extract knowledgeable information from medical data bases Today medical field have come a long way to treat patients with various kind of diseases. Among the most menacing one is the Heart disease which cannot be detected with a stripped eye and comes suddenly when its boundaries are reached. Bad medical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment computer-based and support Systems can be developed to make good decision. Many hospitals use hospital information systems to manage their healthcare or patient data. These systems produce huge amounts of data in the form of images, text, charts and numbers. K nearest neighbor and K means used to support the medical decision making efficiently. Keywords—K Nearest Neighbor, K Means.

ABSTRAK Pelanggan loyal merupakan aset bagi perusahaan. Pengetahuan tentang karakteristik pelanggan merupakan hal yang penting karena dengan pengetahuan itu perusahaan dapat memperlakukan pelanggannya dengan lebih baik, sehingga bisa menjadi pelanggan loyal seterusnya. Penelitian ini bertujuan untuk memetakan atau membuat segmentasi pelanggan menjadi beberapa cluster atau kelompok. Metode yang digunakan pada penelitian ini adalah metode K-Means yang merupakan salah satu metode terbaik dan paling popular dalam algoritma clustering. Untuk mengatasi kelemahan metode K-Means dalam penentuan jumlah cluster, maka digunakan metode Elbow dimana metode ini mendapatkan perbandingan jumlah cluster yang ditambah dengan cara menghitung SSE (Sum of Square Error) dari masing-masing nilai cluster. Dari nilai SSE yang dihitung didapatkan bahwa jumlah cluster terbaik adalah 3, dimana jumlah anggota cluster 1 adalah 47 pelanggan, cluster 2 memiliki 18 pelanggan dan cluster 3 memiliki 8 pelanggan. Pengetahuan ini diharapkan bisa digunakan untuk melakukan strategi pemasaran yang lebih baik. ABSTRACT Loyal customers are an asset to the company. Knowledge of customer characteristics is important because with that knowledge the company can treat their customers better, so it can become loyal customers so on. This study aims to map or create customer segmentation into several clusters or groups. The method used in this research is K-Means method which is one of the best and most popular method in clustering algorithm. To overcome the weakness of the K-Means method in determining the number of clusters, we use the Elbow method where this method gets the comparison of the number of clusters added by calculating the SSE (Sum of Square Error) of each cluster value. From the calculated SSE value, it is found that the best cluster number is 3, where the number of cluster member 1 is 47 customers, cluster 2 has 18 customers and cluster 3 has 8 customers. This knowledge is expected to be used to perform better marketing strategies.

This dissertation investigates harmonic properties of Italian secular songs with Baroque guitar accompaniment in alfabeto tablature, which was a system of letters and other symbols that represented a chord shape to be strummed using all five courses. All of the resulting chords form major or minor triads. These letters were printed in songbooks for guitarists to accompany secular songs. This triadic accompaniment has significant implications for understanding harmonic practices during the seventeenth century.

The purpose of this dissertation is to use computational methods to better understand the harmonic features of the alfabeto song repertoire, which can perhaps be a window through which we can understand the broader harmonic practices of secular genres in the early seventeenth century. Machine learning algorithms are employed to understand the modal framework and harmonic function of the alfabeto corpus. K-means clustering is used to determine how many modes are present within the corpus, building upon the recent work of Albrecht and Huron. To understand the harmonic function within the alfabeto corpus, hidden Markov models analyze the number of functions and which chords belong to each of those functions using a similar methodology to Quinn and White.

The analyses also compare the alfabeto corpus to corpora of other genres and eras such as J.S. Bach, Palestrina, and Franco-Flemish composers. The alfabeto corpus is also compared with itself to better understand its style and any changes over time. This dissertation provides a fresh look at harmonic practice in the seventeenth century by using statistical models that are informed by historical performance practice.

In archaeological applications involving the spatial clustering of two-dimensional spatial data k-means cluster analysis has proved to be a popular method for over 40 years. There are alternatives to k-means analysis which can be thought of as either ‘competitive’ or ‘complementary’ such as k-medoids, model-based clustering, fuzzy clustering and density based clustering among many possibilities. Most of these have been little used in archaeology. That k-means has been a method of choice is probably because it is easily understood, is perceived as being geared to archaeological needs, and was rendered accessible at a time when computational resources were
limited compared to what is now available. It is, in fact, a long-established approach to clustering that pre-dates archaeological interest in it by some years. The theses of the present paper are that
(a) other methods are available that potentially improve on what is possible with k-means;
(b) these are (mostly) as readily understood as k-means;
(c) they are now as easy to implement as k-means is; and
(d) merit more attention than they have received from practitioners who find k-means useful.
The arguments are illustrated by extensive application to a data set that has been the subject of several previous studies.

The technique used for the processing of digital data obtained from pictures is identified as image processing. Plants and crops are ruining because of the excessive use of fertilizers and insecticides. The experts observe the plant disease with their naked eye and identify and detect the type of diseases plant is suffering from. In order to identify infections from input pictures, plant disease detection approach is implemented. An image processing approach is implemented in this research study. This approach is relied on the extraction of textural feature, segmentation and classification. The textural features are extracted from the picture with the help of GLCM algorithm. The input picture is segmented with the help of k-mean clustering algorithm. For classification, the KNN classification is used in this research. This leads to improve accuracy of detection and also leads to classify data into multiple classes. The results of the proposed algorithm are analyzed in terms of various parameters accuracy, precision, recall and execution time. The accuracy of proposed algorithm is increased upto 10 to 15 percent.

Growth of research articles publication in various streams of research is exponential. Searching for a particular article from the research repository is considered to be a tremendous one and also time consuming. Research articles classification based on their respective domain plays an important role for researchers to retrieve articles in a fast manner. Hence a popular search mechanism, namely keyword search has been applied to retrieve appropriate articles, documents, texts, graphs and even relational databases. When new domains of documents are added to the repository it has to identify keywords and add to the corresponding domains for proper classification. A numerical statistic called TF-IDF has been proposed to determine the relevance of word to a document corpus. Clustering algorithms namely Hierarchical, K-Means and Fuzzy C-Means have been used to cluster articles based on the relevance factor TF-IDF. The strength of Fuzzy C-Means clustering has been validated using Silhouette Cluster Validation technique. Finally, performance has been evaluated using Precision, Recall and F-measure and demonstrated that Fuzzy C-Means clustering depicts better accuracy compared to K-Means and Hierarchical clustering.

Indonesia memiliki jumlah penduduk yang cukup besar, Badan Pusat Statistik di tahun 2012 mencatat ada 250.000.000 lebih penduduk yang tersebar di seluruh provinsi Indonesia. Selain itu, Indonesia juga memiliki beragam masalah sosial, salah satunya kemiskinan. Angka garis kemiskinan di Indonesia perlu diperbaiki. Teknik pemanfaatan data menjadi sebuah informasi  baru disebut juga sebagai data mining. Salah satu metode data mining yang cukup populer yaitu clustering dengan menggunakan algoritma k-means. K-means dapat mengolah data tanpa diberitahu lebih dahulu label kelasnya. Penelitian ini akan menghasilkan tiga kelompok provinsi sesuai dengan angka pendapatan sangat rendah, rendah dan cukup. Pengolahan data angka garis kemiskinan di Indonesia menggunakan algoritma k-means mendapatkan hasil davies bouldin index sebesar 0,288. Hasil tersebut dinilai cukup baik sebab semakin dekat hasil yang diperoleh dengan angka nol, maka kemiripan data anggota antar cluster semakin baik.

Abstract: Data Mining is a very interesting area to mine the data for knowledge. Several techniques are available. A data mining is one of the fast growing research field which is used in a wide areas of applications. The data mining consists of classification algorithms, association algorithms and searching algorithms. We are using clustering and decision tree methods to mine the data by using hybrid algorithms K-means, SOM and HAC algorithms from clustering and ID3 and C4.5 and CART algorithms from decision tree and it can produce the better results than the traditional algorithms. The comparative study of these algorithms to obtain which one is high accuracy, frequency, measure, procedure, Pruning, error rate and time complexities from the decision tree algorithm. At the same time the comparative study of these algorithms to obtain which one is quality, error rate, computation time, accessing time, performance, number of cluster, Map topology, type of software, type of data, and data set size from the clustering algorithm.
Key words: K-MEANS, SOM, HAC, ID3, C4.5, CART
Title: Comparative Study of different clustering and Decision Tree for Data Mining Algorithm
Author: Ms. A. Sivasankari,  Mrs. S. Sudarvizhi, S. Radhika Amirtha Bai
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
Research Publish Journals

Department of Informatics in UMS has three concentrations. Due to these three
concentrations, the students are required to choose one of these three concentrations at
the end of the fourth semester. Currently, students determine the concentration based
on the students’ own wishes without a system which gives the students consideration
in selecting their concentration. By designing clustering k-means application, the
researcher expects it can provide the students source of information to determine their
class concentration. The variables used are the value of the dominant subjects of each
concentration ranging from semester 1 to 4, the variables of information system and
enterprise (Basic Web Programming, Algorithms and Programming, Introduction to
Information System, Data Base System) concentrations. Computer network and
multimedia (Data Communication, Computer Network, Practicum Computer Network,
Operation System). Software engineering and animation  (linear algebra and matrix,
Algorithms and Data Structures, Discrete Structure 2, Digital system). The result is
that the students of 2011 academic year data are distributed into 3 cluster, cluster 1
with centroid point (3,333 ; 3,548 ; 3,098) and cluster 2 with centroid point (0,915 ;
1,110 ; 0,773), also cluster 3 with centroid point (2,682 ; 3,221 ; 1,880). The members
of cluster 1 are recommended to take the Computer Network and Multimedia
concentration, the members of cluster 2 are recommended to take the Software
engineering and animation concentration then the members of cluster 3 are
recommended to take the Information System and Enterprise concentration.

Program Studi Informatika di UMS memiliki tiga konsentrasi jurusan. Dengan
adanya tiga konsentrasi penjurusan maka mahasiswa diwajibkan untuk memilih salah
satu dari tiga konsentrasi yang ada, pada akhir semester 4. Saat ini dalam menentukan
konsentrasi hanya berdasarkan dari keinginan mahasiswa itu sendiri tanpa adanya
suatu sistem yang memberikan pertimbangan bagi mahasiswa dalam memilih
konsentrasi jurusan. Dengan melakukan perancangan aplikasi clustering k-means,
diharapkan dapat memberikan sumber informasi penentu kelas konsentrasi bagi
mahasiswa. Variabel yang digunakan nilai mata kuliah yang dominan pada masingmasing

konsentrasi dari semester 1 sampai 4, variabel konsentrasi Sistem Informasi
dan Enterprise (Pemrograman Web Dasar, Algoritma dan Pemrograman, Pengantar
Sistem Informasi, Sistem Basis Data). Jaringan Komputer dan Multimedia
(Komunikasi Data, Jaringan Komputer, Praktikum Jaringan Komputer, Sistem
Operasi). Rekayasa Perangkat Lunak dan Animasi (Aljabar Linier dan Matriks,
Algoritma dan Struktur Data, Struktur Diskret 2, Sistem Digital). Hasil clustering data
mahasiswa 2011 dengan algoritma k-means diperoleh data cluster 1 dengan titik
centroid (3,333 ; 3,548 ; 3,098) dan cluster 2 dengan titik centroid (0,915 ; 1,110 ;
0,773) serta cluster 3 dengan titik centroid (2,682 ; 3,221 ; 1,880). Pada anggota
cluster 1 maka akan direkomendasikan mengambil konsentrasi Jaringan Komputer dan
Multimedia, anggota cluster 2 direkomendasikan untuk konsentrasi Rekayasa
Perangkat Lunak dan Animasi sedangkan anggota cluster 3 direkomendasikan
mengambil konsentrasi Sistem Informasi dan Enterprise.

We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostly-green colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm"s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].

This data mining research uses K-Means Clustering method to classify customer data that produces the number of clusters and customer carasteristic from each cluster. As well as using RFM analysis (Recency, Frequency, Monetary) which presents the number of customers in each cluster. Data used in the process of classifying customers is the sales transaction data from January to September 2017. The result of this research is to produce three customer cluster with customer characteristic from each cluster. Cluster 1 amounted to 679 with low recency  low frequency  low monetary  included into new customers, cluster 2 amounted to 17 with high recency  high frequency  high monetary  included into loyal customers and cluster 3 amounted to 334 with high recency  low frequency  low monetary  including into potentially inactive loyal customers. The results of this clustering can help PT.