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CrossClus: An Overview
- Measure similarity between features by how they group objects into clusters
- Use a heuristic method to search for pertinent features
- Start from user-specified feature and gradually expand search range
- Use tuple ID propagation to create feature values
- Features can be easily created during the expansion of search range, by propagating IDs
- Explore three clustering algorithms: k-means, k-medoids, and hierarchical clustering
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