A Clustering Algorithm Based on Fuzzy-Type Linear Discriminant Analysis and Spatial-Contextual Support Vector Machines
博士 === 國立交通大學 === 電控工程研究所 === 100 === Statistical learning is trying to develop computer algorithms to recognize complex patterns and make decisions based on empirical data automatically. Two major issues are clustering and classification. Clustering organizes patterns into sensible clusters for pat...
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| Format: | Others |
| Language: | en_US |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/78256999129224272289 |
| Summary: | 博士 === 國立交通大學 === 電控工程研究所 === 100 === Statistical learning is trying to develop computer algorithms to recognize complex patterns and make decisions based on empirical data automatically. Two major issues are clustering and classification. Clustering organizes patterns into sensible clusters for patterns in the same cluster to be similar in a sense, whereas classification identifies the categories to which new patterns belong based on an available training set of data containing patterns of known categories. This thesis introduces a fuzzy-based clustering and a spatial-contextual classifier. Fuzzy-based clustering defines within- and between-cluster scatter matrices of a fuzzy-type linear discriminant analysis, and the clustering results are based on the Fisher criterion. The proposed clustering algorithm minimizes the within-cluster information and simultaneously maximizes the between-cluster information. For the classification part, a spatial-contextual term was used to modify the decision function and constraints of a support vector machine. Experimental results show that the proposed methods achieve good clustering and classification performance on famous real data sets.
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