Gu Ruijun
School of Information Science, Nanjing Audit University,Nanjing, 211815, China
Chen Shenglei
School of Information Science, Nanjing Audit University,Nanjing, 211815, China
Wang Jiacai
School of Information Science, Nanjing Audit University,Nanjing, 211815, China
ABSTRACT
Based on clustering consistency, the proposed method first emphasizes the flexibility of the local scale, which means each sample has a corresponding scale parameter. Further more, it overcomes the limitations of traditional methods in all samples with the same global scale parameter. Secondly, it stresses the convenience of parameter selection. It can determine the value of a local scale for one sample by computing the sum of weighted distances of N neighbors. Therefore, it can determine the scale parameter automatically. This study illustrates the proposed algorithm not only has inhibition for certain outliers but is able to cluster the data sets with different scales. Finally, experiments on both, artificial data and UCI data sets show that the proposed method is effective. Some experiments were also performed in image clustering and image segmentation to demonstrate its excellent features in application.
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How to cite this article
Gu Ruijun, Chen Shenglei and Wang Jiacai, 2013. An Adaptive Spectral Clustering Algorithm for Image Clustering and Segmentation. Information Technology Journal, 12: 6763-6769.
DOI: 10.3923/itj.2013.6763.6769
URL: https://scialert.net/abstract/?doi=itj.2013.6763.6769
DOI: 10.3923/itj.2013.6763.6769
URL: https://scialert.net/abstract/?doi=itj.2013.6763.6769
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