Unsupervised Learning Method Based on Partitioning in Data Mining

dc.contributor.advisorBodur, Ersin Kuset
dc.contributor.authorOnyejiaka, Kelechi Churchill
dc.date.accessioned2016-07-18T09:54:46Z
dc.date.available2016-07-18T09:54:46Z
dc.date.issued2015-05
dc.date.submitted2015
dc.departmentEastern Mediterranean University, Faculty of Arts and Science, Department of Mathematicsen_US
dc.descriptionMaster 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.abstractThis 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 applicationsen_US
dc.description.abstractÖZ: 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 uygulamalarıen_US
dc.identifier.citationOnyejiaka, 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.urihttps://hdl.handle.net/11129/2842
dc.language.isoen
dc.publisherEastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ)en_US
dc.relation.publicationcategoryTez
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMathematicsen_US
dc.subjectApplied Mathematics and Computer Scienceen_US
dc.subjectCluster analysis - Data miningen_US
dc.subjectData miningen_US
dc.subjectdata mining algorithmsen_US
dc.subjectdata mining applicationsen_US
dc.titleUnsupervised Learning Method Based on Partitioning in Data Miningen_US
dc.typeMaster Thesis

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