Abstract
Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better than pure or normalized feature space. Therefore, running a simple clustering algorithm on generated feature space can obtain the final partition significantly better than pure data. In this method, we use a modification of k-means for initial clustering runs named as “Intelligent k-means”, which is especially defined for clustering ensembles. The results of the proposed method are presented using both simple k-means and intelligent k-means. Fast convergence and appropriate behavior are the most interesting points of the proposed method. Experimental results on real data sets show effectiveness of the proposed method.
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Azimi, J., Abdoos, M., Analoui, M. (2007). A New Efficient Approach in Clustering Ensembles. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_41
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DOI: https://doi.org/10.1007/978-3-540-77226-2_41
Publisher Name: Springer, Berlin, Heidelberg
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