Pattern Clustering Using Incremental Splitting for Non-Uniformly Distributed Data
نویسندگان
چکیده
This article reports on our work on the clustering of non-uniformly distributed data. An innovative method, termed incremental splitting, is presented. Taking the K-means method as the core, the proposed approach splits only clusters with the largest total error in each iteration. This heuristic has the effect of allocating more clusters to those regions having more sample data. Consistent experimental results reveal that our method outperforms commonly used heuristics, including random initialization, binary splitting and pair-wise nearest neighbour.
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