MobileMiner: a real world case study of data mining in mobile communication

ABSTRACT

Mobile communication data analysis has been often used as a background application to motivate many data mining problems. However, very few data mining researchers have a chance to see a working data mining system on real mobile communication data. In this demo, we showcase our new system MobileMiner on a real mobile communication data set, which presents a case study of business solutions using state-of-the-art data mining techniques. MobileMiner adaptively profiles users' behavior from their calling and moving record streams. Customer segmentation and social community analysis can be conducted based on user profiles. We show how data mining techniques can help in mobile communication data analysis. Moreover, we also show some interesting observations which still cannot be mined by the current techniques, and thus may motivate new research and development.

REFERENCES

• 1: Lei Chang , Tengjiao Wang , Dongqing Yang , Hua Luan, SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows, Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, p.83-92, December 15-19, 2008 [doi>10.1109/ICDM.2008.36]
• 2: Yizong Cheng , George M. Church, Biclustering of Expression Data, Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, p.93-103, August 19-23, 2000
• 3: Daxin Jiang , Jian Pei , Murali Ramanathan , Chuan Lin , Chun Tang , Aidong Zhang, Mining gene–sample–time microarray data: a coherent gene cluster discovery approach, Knowledge and Information Systems, v.13 n.3, p.305-335, November 2007 [doi>10.1007/s10115-006-0031-9]
• 4: Jian Pei , Daxin Jiang , Aidong Zhang, On mining cross-graph quasi-cliques, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081898]
• 5: W. Zhou, J. Wen, W. Ma, and H. Zhang. A concentric-circle model for community mining in graph structures. In Microsoft Research, Seattle, Technical. Report MSR-TR-2002-123, 2002.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Algorithms, Performance

Keywords:
community discovery, data stream mining, mobile communication data, sequence clustering