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An improved density-sensitive semi-supervised clustering algorithm

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3 Author(s)
Yulong Wu, ; MOE-MS Key Laboratory of Multimedia Calculation and Communication, University of Science and Technology of China, Hefei, 230027, China ; Pingbo Yuan, ; Nenghai Yu,

This paper presents an improved density-sensitive distance measurement, which can effectively enlarge the distances among data points in different high density regions and shorten the distances among data points in the same high density region. Furthermore, a semi-supervised learning algorithm named improved density-sensitive semi-supervised clustering (IDS-SC) algorithm is introduced based on this distance measurement. The results demonstrate the superiority of IDS-SC in the application of Coral image set.

Published in:

Visual Information Engineering, 2008. VIE 2008. 5th International Conference on

Date of Conference:

July 29 2008-Aug. 1 2008