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
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fuzzy Sets and Systems
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A scalable framework for cluster ensembles
Pattern Recognition
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
This paper addresses the task of helping investigators identify characteristics in credit-card frauds, so as to establish fraud profiles. To do this, a clustering methodology based on the combination of an incremental variant of the linearised fuzzy c-medoids and a hierarchical clustering is proposed. This algorithm can process very large sets of heterogeneous data, i.e. described by both categorical and numeric features. The relevance of the proposed approach is illustrated on a real dataset containing next to one million fraudulent transactions.