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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aditya Prakash, B., Sridharan, A., Seshadri, M., Machiraju, S., Faloutsos, C.: EigenSpokes: Surprising patterns and scalable community chipping in large graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6119, pp. 435–448. Springer, Heidelberg (2010), http://www.cs.cmu.edu/badityap/papers/eigenspokes-pakdd10.pdf
BBC News, Over 5 billion mobile phone connections worldwide (2010), http://www.bbc.co.uk/news/10569081
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intelligence 1, 224–227 (1979)
Eagle, N. (2010), The Reality Mining Data README, http://eprom.mit.edu/data/RealityMining_ReadMe.pdf
Eagle, N., Pentland, A., Lazer, D.: Inferring Social Network Structure using Mobile Phone Data. Proc. of the National Academy of Sciences 106(36), 15274–15278 (2009)
Farrugia, M., Quigley, A.: Cell phone Mini Challenge: Node-link animation award animating multivariate dynamic social networks. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 215–216 (2008)
Hohwald, H., Frías-Martínez, E., Oliver, N.: User modeling for telecommunication applications: Experiences and practical implications. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 327–338. Springer, Heidelberg (2010)
Joshi, A., Krishnapuram, R.: Robust Fuzzy Clustering Methods to Support Web Mining. In: Proceedings of the Workshop on Data Mining and Knowledge Discovery, SIGMOD 1998, pp. 15/1–15/8 (1998)
Lingras, P.: Unsupervised Rough Set Classification using GAs. Journal Of Intelligent Information Systems 16(3), 215–228 (2001)
Lingras, P.: Rough set clustering for Web mining. In: Proceedings of 2002 IEEE International Conference on Fuzzy Systems (2002)
Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough K-means. Journal of Intelligent Information Systems 23(1), 5–16 (2004)
Lingras, P., Hogo, M., Snorek, M., Leonard, B.: Clustering Supermarket Customers Using Rough Set Based Kohonen Networks. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 169–173. Springer, Heidelberg (2003)
Lingras, P., Hogo, M., Snorek, M.: Interval Set Clustering of Web Users using Modified Kohonen Self-Organizing Maps based on the Properties of Rough Sets. Web Intelligence and Agent Systems: An International Journal 2(3), 213–217 (2004)
MacQueen, J.: Some Methods fir Classification and Analysis of Multivariate Observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Nanavati, A.A., Gurumurthy, S., Das, G., Chakraborty, D., Dasgupta, K., Mukherjea, S., Joshi, A.: On the structural properties of massive telecom call graphs: findings and implications. In: Proc. of 15th ACM Conference on Information and Knowledge Management, pp. 435–444 (2006)
Peters, J.F., Skowron, A., Suraj, Z., Rzasa, W., Borkowski, M.: Clustering: A rough set approach to constructing information granules. In: Proceedings of 6th International Conference Soft Computing and Distributed Processing on SCDP 2002, pp. 57–61 (2002)
Seshadri, M., Machiraju, S., Sridharan, A., Bolot, J., Faloutsos, C., Leskovec, J.: Mobile Call Graphs: Beyond Power-Law and Lognormal Distributions. In: Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008), http://cs.stanford.edu/people/jure/pubs/dpln-kdd08.pdf
Skowron, A., Stepaniuk, J.: Information granules in distributed environment. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 357–366. Springer, Heidelberg (1999)
R, The R Project for Statistical Computing (2010), http://www.r-project.org
Ye, Q., Zhu, T., Hu, D., Wu, B., Du, N., Wang, B.: Cell phone mini challenge award: Social network accuracy - exploring temporal communication in mobile call graphs. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 207–208 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lingras, P., Bhalchandra, P., Mekewad, S., Rathod, R., Khamitkar, S. (2011). Comparing Clustering Schemes at Two Levels of Granularity for Mobile Call Mining. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_87
Download citation
DOI: https://doi.org/10.1007/978-3-642-24425-4_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
eBook Packages: Computer ScienceComputer Science (R0)