Unsupervised Learning Method Based on Partitioning in Data Mining
| 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.department | Eastern Mediterranean University, Faculty of Arts and Science, Department of Mathematics | en_US |
| 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.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 | https://hdl.handle.net/11129/2842 | |
| dc.language.iso | en | |
| dc.publisher | Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ) | en_US |
| dc.relation.publicationcategory | Tez | |
| 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 | Master Thesis |
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