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Evaluation of Clustering Validity | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 20, Volume 5, Issue 2, 2008, Pages 79-97 PDF (469.35 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2008.163987 | ||
| Authors | ||
| Rudhwan Yousif Sideek1; Ghaydaa A.A. Al-Talib2 | ||
| 1Technical College Technical Education Authority / Mosul | ||
| 2College of Computer Sciences and mathematics University of Mosul, Mosul, Iraq | ||
| 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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