Abstract
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.
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Strehl, A., Ghosh, J. (2000). A Scalable Approach to Balanced, High-Dimensional Clustering of Market-Baskets. In: Valero, M., Prasanna, V.K., Vajapeyam, S. (eds) High Performance Computing — HiPC 2000. HiPC 2000. Lecture Notes in Computer Science, vol 1970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44467-X_48
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DOI: https://doi.org/10.1007/3-540-44467-X_48
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