On relational data versions of c-means algorithms
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
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The paper proposes a scalable incremental clustering algorithm to process heterogeneous data-streams, described by both categorical and numeric features, and its application to the domain of credit-card fraud analysis, to establish dynamic frauds profiles. The aim is to identify subgroups of frauds exhibiting similar properties and to study their temporal evolution and, in particular, the emergence of fraudster behaviours. The application to real data corresponding to a one year fraud stream highlights the relevance of the approach that leads to the identification of significant profiles.