| Paper: | MLSP-P6.8 | ||
| Session: | Learning Theory and Models | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | A NOVEL CLUSTERING METHOD WITH NETWORK STRUCTURE BASED ON CLONAL ALGORITHM | ||
| Authors: | Jie Li; Xidian University | ||
| Xinbo Gao; Xidian University | |||
| Licheng Jiao; Xidian University | |||
| Abstract: | In the field of cluster analysis, objective function based clustering algorithm is one of widely applied methods. However, this type of algorithms need the priori knowledge about the cluster number and the form of clustering prototypes, which 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. For the purpose, this paper presents a novel clustering method with network structure based on clonal algorithm to realize the automation of cluster analysis. By analyzing the neurons of the obtained 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 more effective performance on cluster analyzing the data set with mixed numeric values and categorical values. | ||
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