|
EMU I-REP >
08 Faculty of Arts and Sciences >
Department of Mathematics >
Theses (Master's and Ph.D) – Mathematics >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11129/2842
|
Title: | Unsupervised Learning Method Based on Partitioning in Data Mining |
Authors: | Bodur, Ersin Kuset Onyejiaka, Kelechi Churchill Eastern Mediterranean University, Faculty of Arts and Science, Department of Mathematics |
Keywords: | Mathematics Applied Mathematics and Computer Science Cluster analysis - Data mining Data mining data mining algorithms data mining applications |
Issue Date: | May-2015 |
Publisher: | Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ) |
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. |
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 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 |
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. |
URI: | http://hdl.handle.net/11129/2842 |
Appears in Collections: | Theses (Master's and Ph.D) – Mathematics
|
This item is protected by original copyright
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|