Mining Multi-Patterns in Pattern-Based Clustering
Qian Ma, Jingfeng Guo · Procedia Engineering · 2012
Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, pattern-based clustering finds objects that exhibit coherent patterns in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications. However, most of previous approaches based on single pattern model can only explore one of specific patterns, not both of them. This paper analyses different kinds of patterns between items, presents the conception of multi-pattern model. Based on this model, we can capture patterns of shifting, scaling, and other patterns with the same feathers simultaneously. From the study of multi-pattern model's characters and operating principles, an effective algorithm is introduced to cluster objects which are coherent with multi-patterns.