Fuzzy Classification of Incomplete Data with Adaptive Volume

For solving the incomplete data problem of missing feature values in prototype data, various strategies were proposed. In this paper, two improved approaches are proposed to estimate the missing values of incomplete data. The two approaches are based on combining the adaptive volume Gustafson-Kessel...

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Bibliographic Details
Main Authors: Yao, L., Kuei-Sung Weng, Ren-Wei Chang
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computer simulation
Covariance matrix
Database systems
Deductive databases
Fuzzy systems
Genetic algorithms
Gradient methods
Principal component analysis
Prototypes
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Summary:For solving the incomplete data problem of missing feature values in prototype data, various strategies were proposed. In this paper, two improved approaches are proposed to estimate the missing values of incomplete data. The two approaches are based on combining the adaptive volume Gustafson-Kessel algorithm (GKA) and the nearest vector features under the distance norm evaluated by complete data. The GKA with adaptive volume is applied for clustering and classifying the results. At last, compared the result with other strategies, and the computer simulations show that the improved strategies provide superior effects.
DOI:10.1109/ACIIDS.2009.58