Unsupervised Learning Method Based on Partitioning in Data Mining

EMU I-REP

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dc.contributor.advisor Bodur, Ersin Kuset
dc.contributor.author Onyejiaka, Kelechi Churchill
dc.date.accessioned 2016-07-18T09:54:46Z
dc.date.available 2016-07-18T09:54:46Z
dc.date.issued 2015-05
dc.date.submitted 2015
dc.identifier.citation Onyejiaka, Kelechi Churchill (2015). Unsupervised Learning Method Based on Partitioning in Data Mining. . Thesis (M.S.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Mathematics, Famagusta: North Cyprus. en_US
dc.identifier.uri http://hdl.handle.net/11129/2842
dc.description Master of Science in Applied Mathematics and Computer Science. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Arts and Sciences, Dept. of Mathematics, 2015. Supervisor: Assist. Prof. Dr. Ersin Kuset Bodur. en_US
dc.description.abstract This study provides the introduction of some basic definitions about clustering method of data mining. For this purpose, it is given the methods of data mining, some algorithms of clustering method. Meanwhile, the k -Means clustering and Hierarchical clustering algorithms are defined. The aim of this study is to cluster the dataset into two clusters using Hierarchical clustering algorithm and k -Means algorithm. In order to achieve our target, two distance formulas are used to measure the distance between the vectors in the algorithms: the Euclidean distance and k -Nearest neighborhood distance.to compare two methods. Keywords: Data mining, data mining algorithms, data mining applications en_US
dc.description.abstract ÖZ: Bu çalışma veri madenciliği kümeleme yönteminin bazı temel tanımlarını sunar. Bu amaçla, veri madenciliği yöntemleri, veri madenciliğinin bazı kümeleme yöntemleri algoritmaları veriliyor. Bunun yanında, K -ortalama ve Hiyerarşik kümeleme algoritmaları tanımlanır. Bu çalışmanın amacı, Hirerarşik ve K -ortalama algoritmalarını kullanıp veri kümesini iki kümeye ayırmaktır. Amacımıza ulaşmak için, vektörler arasındaki uzaklığı ölçmek için iki tane tanım kullanılır: Öklit uzaklık ve en yakın K komşu bağıntıları. Anahtar kelimeler: Veri madenciliği teknikleri, veri madenciliği algorimaları, veri madenciliği uygulamaları en_US
dc.language.iso eng en_US
dc.publisher Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ) en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Mathematics en_US
dc.subject Applied Mathematics and Computer Science en_US
dc.subject Cluster analysis - Data mining en_US
dc.subject Data mining en_US
dc.subject data mining algorithms en_US
dc.subject data mining applications en_US
dc.title Unsupervised Learning Method Based on Partitioning in Data Mining en_US
dc.type masterThesis en_US
dc.contributor.department Eastern Mediterranean University, Faculty of Arts and Science, Department of Mathematics en_US


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