Entropy based soft K-means clustering

In machine learning or data mining research area, clustering is definitely an active topic and has drawn a lot of attention for its significance in practical applications, such as image segmentation, data analysis, text mining and so on. There have been a great number of clustering algorithms derive...

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Bibliographic Details
Main Authors: Xue Bai, Siwei Luo, Yibiao Zhao
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Distance measurement
Entropy
Information theory
Probabilistic logic
Random variables
Uncertainty
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Summary:In machine learning or data mining research area, clustering is definitely an active topic and has drawn a lot of attention for its significance in practical applications, such as image segmentation, data analysis, text mining and so on. There have been a great number of clustering algorithms derived from different points of view. K-means is widely known as a straightforward and fairly efficient method for solving unsupervised learning problems. Due to its inherent weaknesses in some cases, many enhancements have been made for it. Soft k-means algorithm is one of them. In this article, we propose an entropy based soft k-means clustering method which utilizes the entropy and relative entropy information from data samples to guide the training process, for reaching a better clustering result.
DOI:10.1109/GRC.2008.4664627