Effect of Parameter Selection on Fuzzy Clustering
Journal Title: Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi - Year 2018, Vol 2, Issue 1
Abstract
Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. Cluster analysis seeks to partition given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. Clustering can be performed in hard or fuzzy mode. One of the important conditions in order to reach accurate results in clustering analysis is to determine the initial parameters. In many studies, researchers do not have prior information about the number of clusters. Clustering algorithms in general need the number of clusters as a prior, which is mostly hard for domain expert to estimate. In this work, in order to overcome this problem, cluster validity indices in literature were reviewed and these indices were used in genetic data set. The result was simply analyzed and according to the analysis, validity indices do not always discover the optimal number of clusters.
Authors and Affiliations
Özer Özdemir, Aslı Kaya
Keywords
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- EP ID EP418290
- DOI 10.31200/makuubd.348688
- Views 138
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