Algorithms for clustering data
Algorithms for clustering data
A unified geometric approach to graph separators
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Scalable algorithms for mining large databases
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Algorithms
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
CoFD: An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
The impact of semi-supervised clustering on text classification
Proceedings of the 17th Panhellenic Conference on Informatics
Consensus strategy for clustering using RC-images
Pattern Recognition
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This paper presents Opossum, a novel similarity-based clustering approach based on constrained, weighted graph-partitioning. Opossum is particularly attuned to real-life market baskets, characterized by very high-dimensional, highly sparse customer-product matrices with positive ordinal attribute values and significant amount of outliers. Since it is built on top of Metis, a well-known and highly efficient graphpartitioning algorithm, it inherits the scalable and easily parallelizeable attributes of the latter algorithm. Results are presented on a real retail industry data-set of several thousand customers and products, with the help of Clusion, a cluster visualization tool.