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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Comparing Clustering Schemes at Two Levels of Granularity for Mobile Call Mining
verfasst von : Pawan Lingras, Parag Bhalchandra, Satish Mekewad, Ravindra Rathod, Santosh Khamitkar
Erschienen in: Rough Sets and Knowledge Technology
Verlag: Springer Berlin Heidelberg
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Datasets in many applications can be viewed at different levels of granularity. Depending on the level of granularity, data mining techniques can produce different results. Correlating results from different levels of granularity can improve the quality of analysis. This paper proposes a process and measures for comparing clustering results from two levels of granularity for a mobile call dataset. The clustering is applied to the phone calls as well as phone numbers, where phone calls are finer granules while phone numbers are coarser granules. The coarse granular clustering is then expanded to a finer level and finer granular clustering is contracted to the coarser granularity for additional qualitative analysis. The paper uses a popular cluster quality measure called Davies-Bouldin index as well as a proposal for transforming clustering schemes between different levels of granularity.