K-means based clustering on mobile usage for social network analysis purpose

Xu Yang, Yapeng Wang, Dan Wu, Athen Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

The development of mobile network technology provides a great potential for social networking services. This paper studied data mining for social network analysis purpose, which aims at find people's social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT's Reality Mining project is employed. The classification model presented in this project provides a new approach to find the proximity between users - based on their registration frequencies to specific cellular towers associated their working places. K-means Algorithm is applied for clustering, and we find the result could achieve the highest accuracy 0.823 at the number groups k = 6. The clustering result successfully reflects the higher proximity (at work) for the intraclass subjects.

Original languageEnglish
Title of host publicationProc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications
Pages223-228
Number of pages6
Publication statusPublished - 2010
Event6th International Conference on Advanced Information Management and Service, IMS2010, with 2nd International Conference on Data Mining and Intelligent Information Technology Applications, ICMIA2010 - Seoul, Korea, Republic of
Duration: 30 Nov 20102 Dec 2010

Publication series

NameProc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications

Conference

Conference6th International Conference on Advanced Information Management and Service, IMS2010, with 2nd International Conference on Data Mining and Intelligent Information Technology Applications, ICMIA2010
Country/TerritoryKorea, Republic of
CitySeoul
Period30/11/102/12/10

Keywords

  • Clustering
  • Data mining
  • K-Means
  • Social network analysis

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