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AL-Rafidain Journal of Computer Sciences and Mathematics

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Evaluation of Clustering Validity

    Rudhwan Yousif Sideek Ghaydaa A.A. Al-Talib

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 2, Pages 79-97
10.33899/csmj.2008.163987

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Abstract

Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. As a consequence, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity.
            In this paper, we present a clustering validity procedure, which evaluates the results of clustering algorithms on data sets. We define a validity indexes, S_Dbw & SD, based on well-defined clustering criteria enabling the selection of the optimal input parameters values for a clustering algorithm that result in the best partitioning of a data set.
            We evaluate the reliability of our indexes experimentally, considering clustering algorithm (K_Means) on real data sets.
Our approach is performed favorably in finding the correct number of clusters fitting a data set.
 
Keywords:
    Data Mining K_Means S_Dbw SD
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(2008). Evaluation of Clustering Validity. AL-Rafidain Journal of Computer Sciences and Mathematics, 5(2), 79-97. doi: 10.33899/csmj.2008.163987
Rudhwan Yousif Sideek; Ghaydaa A.A. Al-Talib. "Evaluation of Clustering Validity". AL-Rafidain Journal of Computer Sciences and Mathematics, 5, 2, 2008, 79-97. doi: 10.33899/csmj.2008.163987
(2008). 'Evaluation of Clustering Validity', AL-Rafidain Journal of Computer Sciences and Mathematics, 5(2), pp. 79-97. doi: 10.33899/csmj.2008.163987
Evaluation of Clustering Validity. AL-Rafidain Journal of Computer Sciences and Mathematics, 2008; 5(2): 79-97. doi: 10.33899/csmj.2008.163987
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