Comparing clustering schemes at two levels of granularity for mobile call mining

Authors:
Pawan Lingras;Parag Bhalchandra;Satish Mekewad;Ravindra Rathod;Santosh Khamitkar
Affiliations:
School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India and Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nov ...;School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India;School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India;School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India;School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

Citing 8
Cited 0

Unsupervised Rough Set Classification Using GAs

Journal of Intelligent Information Systems
Information Granules in Distributed Environment

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
On the structural properties of massive telecom call graphs: findings and implications

CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets

Web Intelligence and Agent Systems
Mobile call graphs: beyond power-law and lognormal distributions

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
EigenSpokes: surprising patterns and scalable community chipping in large graphs

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
User modeling for telecommunication applications: experiences and practical implications

UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.