0-SM: A fast algorithm for mining Candidate Clusters in Pattern-based Clustering
Jingfeng Guo, Qian Ma, Hanfeng Liu · 2007
Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including large scale scientific data analysis, target marketing, Web usage analysis, etc. However, state-of-the-art pattern-based clustering methods (e.g., the sigma-pCluster algorithm), mining candidate clusters mostly by comparing each pair of attributes and objects, which have reduced the efficiency and makes them inappropriate for many real-life applications. This paper present a fast algorithm for mining candidate clusters. We called it Zero-Sub-Matrix. It has a better efficiency than previous algorithms.