Unsupervised Rough Set Classification Using GAs
Journal of Intelligent Information Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering validity checking methods: part II
ACM SIGMOD Record
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
Some refinements of rough k-means clustering
Pattern Recognition
Web Intelligence and Agent Systems
Mining from incomplete quantitative data by fuzzy rough sets
Expert Systems with Applications: An International Journal
Fuzzy C-means clustering of web users for educational sites
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Correlating Fuzzy and Rough Clustering
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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Mobile communication devices are gaining even faster acceptance than the proliferation of web in 1990's. Mobile communication spans a wide variety of protocols ranging from phone calls, text messages/SMS, e-mail data, web data, to social networking. Characterization of users is an important issue in the design and maintenance of mobile services with unprecedented commercial implications. Analysis of the data from the mobile devices faces certain challenges that are not commonly observed in the conventional data analysis. The likelihood of bad or incomplete mobile communication data is higher than the conventional applications. The clusters and associations in phone call mining do not necessarily have crisp boundaries. Researchers have studied the possibility of using fuzzy sets in clustering of web resources. The issues from web mining are further compounded due to multi-modal communication in the mobile world. This paper compares crisp and fuzzy clustering of a mobile phone call dataset. This emerging area of application is called mobile phone call mining, which involves application of data mining techniques to discover usage patterns from the mobile phone call data. The analysis includes comparison of centroids, cluster quality of crisp and fuzzy clustering schemes and analysis of their semantics. Since fuzzy clustering is descriptive, equivalent rough clustering schemes are used for succinct comparison of cluster sizes.