A NEW APPROACH TO DATA CLUSTERING USING A COMPUTATIONAL VISUAL ATTENTION MODEL

Cluster analysis plays an important role in many respects such as knowledge discovery, data mining and information retrieval. In this paper we propose a new approach inspired by the early vision. system, of the primate for data clustering. Human beings are able to locate key points that contains more important information in a complex scene. To realize this function, our approach uses a computational visual attention model that selects and extracts salient areas in visual field by local difference features. Then the extracted salient areas in original visual field can be regarded as the clusters in the data feature space. Without prior knowledge, this attention Model based approach Can identify data clusters with arbitrary shapes at different scales. Finally our algorithm has been tested in the evaluation experiments on the benchmark datasets to show its competitive performance.