The Discretization of Continuous Attributes Based on Improved SOM Clustering

Gang Tao; Yong Gang Yan; Jiao Zou; Jun Liu

doi:10.4028/www.scientific.net/AMM.701-702.88

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 701-702The Discretization of Continuous Attributes Based...

The Discretization of Continuous Attributes Based on Improved SOM Clustering

Abstract:

In order to solve the problem of continuous attribute discretization, a new improved SOM clustering algorithm was proposed. The algorithm uses the SOM to achieve the initial cluster and get the clustering up limit, then treats the initial cluster centers as samples and use the BIRCH hierarchical clustering algorithm to get secondary clustering, then solves the problems of inflated clusters and identifies discrete breakpoints set. Finally, find the nearest neighbors of each cluster center among these any samples of Breakpoints sets which belong to its attribute, and use it as a basis of discrete trimming. The experimental results show that the proposed algorithm outperforms the conventional discrete SOM clustering algorithm in the breakpoints set (contour factor to enhance 75%) and discrete accuracy (incompatible degrees closer to 0) aspects.

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Periodical:

Applied Mechanics and Materials (Volumes 701-702)

Pages:

88-93

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.701-702.88

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Online since:

December 2014

Authors:

Gang Tao, Yong Gang Yan*, Jiao Zou, Jun Liu

Keywords:

Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH), Discretization, Nearest Neighbor, Neural Network, Self-Organizing Feature Map (SOM), Silhouette Coefficient

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* - Corresponding Author

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