臺灣博碩士論文加值系統

摘要
在模糊聚類中，模糊C均值(FCM)演算法是應用最為廣泛的聚類方法。各種基於FCM的擴展演算法在過去許多文獻中被發表出來。然而，FCM及其擴展的演算法通常會因為需要事先給定群集數量而受到起始值和參數的選擇的影響，儘管有一些方法可以解決FCM的上述問題，但在沒有模糊指標m且事先不給定聚類數量和參數的情況下，FCM沒辦法同時達到穩健於起始值與參數的選擇。FCM也存在著對區間型態的測量尺度進行分群的限制。在本篇論文中，我們將穩健式學習模糊C均值聚類演算法(其根據於模糊C均值演算法) 拓展到區間資料並稱之為區間資料之穩健式模糊C均值聚類演算法（I-RLFCM），以顯示I-RLFCM演算法在區間資料集的有效性。

Abstract
In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most widely used clustering method. Many extensions of FCM had been proposed in the literature. However, the FCM algorithm and its extensions are usually affected by initializations and parameter selection with a number of clusters to be given a priori. Although there were some works to solve these problems in FCM, there is no work for FCM to be simultaneously robust to initializations and parameter selection under free of the fuzziness index m without a given the number of clusters and parameters in priori. The FCM also have a restriction for classify the interval type of measurement scale. In this thesis, we extend the robust-learning fuzzy c-means clustering algorithm to interval data and called it robust-learning fuzzy c-means clustering algorithm for interval data (I-RLFCM) where based on the fuzzy c-means algorithm to demonstrate the effectiveness of the I-RLFCM algorithm for interval datasets.

Contents
摘要------------------------------I
Abstract-------------------------II
Contents------------------------III
List of Figures------------------IV
List of Tables--------------------V
Chapter 1. Introduction-----------1
Chapter 2. Fuzzy c-means and robust-learning fuzzy c-means clustering algorithms-------4
2.1 Fuzzy c-means clustering algorithm-------4
2.2 Robust-learning Fuzzy c-means algorithm-------5
Chapter 3. Robust-learning fuzzy c-means clustering algorithms for interval data----------8
3.1 Introduction for interval data-------------8
3.2 Fuzzy c-means clustering algorithm for interval data-------------9
3.3 Fuzzy validity indices for interval data----------11
3.4 Robust-learning Fuzzy c-means algorithm for interval data-------------13
Chapter 4. Simulation and comparisons------------20
Chapter 5. Conclusions-------------30
References--------------31

List of Figures
Fig. 1 Interval data from example----------------9
Fig. 2 Flowchart of I-RLFCM---------------------19
Fig. 3 Clustering results of example 1----------21
Fig. 4 Clustering results of example 2----------23
Fig. 5 Clustering results of example 3----------24

List of Tables
Table 1 Validity index values of example 1------22
Table 2 Validity index values of example 2------23
Table 3 Validity index values of example 3------25
Table 4 Clustering result of I-RLFCM and IFCM (c=3) and fetal state in example 4-----26
Table 5 Percentage of the fetal state in example 4 with c=3------27
Table 6 Percentage of the clusters in example 4 with c=3-----27
Table 7 Clustering result of I-RLFCM and IFCM (c=4) and fetal state in example 4-----28
Table 8 Percentage of the fetal state in example 4 with c=4-----28
Table 9 Percentage of the clusters in example 4 with c=4-----28
Table 10 Validity index values in example 4-----29
Table 11 Clustering result of I-RLFCM and IFCM (c=2) and fetal state in example 4------29

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