Fuzzy Clustering of Complete and Incomplete Data Based on Nearest Cluster and Linear Regression
| Autor: | Ren-Wei Chang, 張仁維 |
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| Rok vydání: | 2009 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 97 In fuzzy clustering field, Gustafson-Kessel Algorithm assumes a fixed volume for each ellipsoid decision regions. However, Due to the difference of data distribution, the volume of decision regions should be fine tuned for different kinds of clusters to improve the accuracy of fuzzy clustering. For the drawback of G-K algorithm, a fuzzy clustering algorithm with adaptive learning of ellipsoid decision regions is proposed in this thesis. By using Particle Swarm Optimization to find the optimal volume of each cluster, the decision region of each cluster can be adapted iteratively for different kinds of data distribution, thus the decision can be adjusted more correctly by the effect of adaptive learning and the error caused by different data distribution can be efficiently decreased. In real world, due to weak signal of measurement, improper operation or equipment malfunction, data features may have missing components. It leads to incomplete data problem. For solving incomplete problem, many strategies have been proposed. In this thesis, by analyzing complete part of prototype data, nearest cluster and multiple linear regression models, two improved strategies for estimating missing values are given. Numerical simulations of artificial and real data sets were used to verify the efficiency of proposed strategies. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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