An Approximate Fisher Linear Discriminant Analysis for Clustering

• Main Authors: Yang, Cheng-Gang, 楊承綱
• Other Authors: Jou, Chi-Cheng
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/27461978138598350716

碩士 === 國立交通大學 === 電控工程研究所 === 99 === In the era we get the large amounts of data more and more easily, the data clustering becomes more and more important. The difficulty of clustering is that every case has many statistics which call features, how we choose these features or their combination will effect the clustering result extremely. Principal component analysis (PCA) is one of the common feature extraction methods, but extracting the components of maximum variance is uncertain best for both classification and clustering. This thesis focuses on improving the feature extraction, we combine Fisher linear discriminant (FLD) which can extract the features excellently for classification and the traditional K-means clustering to an approximate Fisher linear discriminant (AFD) algorithm. Let the K-means clustering result is the known class, then use FLD to find the best features, after that, use these features to cluster and then do FLD again, we also get the best features for this new clustering result. Repeat above process until convergence. This thesis chooses two kinds of the data, Iris and Wine, that have three classes to do experiment, and compare the clustering accuracy by the real class. By experiment we find that even though the components of maximum variance can contain the most information of the original data, but it is not useful for clustering. Extracting the key features by AFD algorithm to cluster is better than PCA, and in the same number of features AFD algorithm has better clustering result than PCA.