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Clustering algorithm in high-dimension based on similarity

A new clustering algorithm for complex attributes was proposed based on feature similarity measurement idea in this paper. In the algorithm, the objects similarities were measured by complex attributes similarity function. Then, a graph model was constructed based on the similarity. Finally, the graph was divided to clusters. Compared with the traditional clustering algorithms based on selecting dimension and decreasing dimension, the proposed algorithm can process high-dimension data and complex attributes effectively. Meanwhile, it does not need reviewing original data when modifying parameter. The clustering performance of the algorithm is demonstrated with real data sets and the experiment results show that the new clustering algorithm is more accurate and effective than the previous algorithms.