Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Constructing and application of multimedia TV-news archives
Expert Systems with Applications: An International Journal
An EM based multiple instance learning method for image classification
Expert Systems with Applications: An International Journal
A polygon description based similarity measurement of stock market behavior
Expert Systems with Applications: An International Journal
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
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This paper proposes new variance enhanced clustering methods to improve the popular K-medoid algorithm by adapting variance information in data clustering. Since measuring similarity between data objects is simpler than mapping data objects to data points in feature space, these pairwise similarity based clustering algorithms can greatly reduce the difficulty in developing clustering based pattern recognition applications. A web-based image clustering system has been developed to demonstrate and show the clustering power and significance of the proposed methods. Synthetic numerical data and real-world image collection are applied to evaluate the performance of the proposed methods on the prototype system. As shown as the web-demonstration, the proposed method, variance enhanced K-medoid model, groups similar images in clusters with various variances according to the distribution of image similarity values.