Abstract
In the field of cluster analysis, objective function based clustering algorithm is one of widely applied methods so far. However, this type of algorithms need the priori knowledge about the cluster number and the type of clustering prototypes, and can only process data sets with the same type of prototypes. Moreover, these algorithms are very sensitive to the initialization and easy to get trap into local optima. To this end, this paper presents a novel clustering method with fuzzy network structure based on limited resource to realize the automation of cluster analysis without priori information. Since the new algorithm introduce fuzzy artificial recognition ball, operation efficiency is greatly improved. By analyzing the neurons of network with minimal spanning tree, one can easily get the cluster number and related classification information. The test results with various data sets illustrate that the novel algorithm achieves much more effective performance on cluster analyzing the large data set with mixed numeric values and categorical values.
This project was supported by NFSC (N0.60202004).
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Jie, L., Xinbo, G., Licheng, J. (2004). A Novel Clustering Algorithm Based on Immune Network with Limited Resource. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_29
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DOI: https://doi.org/10.1007/978-3-540-30549-1_29
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