1. A method of defect image classification through integrating image analysis and data mining, comprising steps of: (a) defect detection, wherein a plurality of defect images are obtained; and an outline of a first defect of each said defect image are detected with two-stage parameter adjustment to obtain a position, a shape and a size of said first defect in each said defect image; (b) feature extraction and similarity measurement, wherein a plurality of defect patterns are obtained; and said defect patterns comprises a pattern of bright white defect (MC), a pattern of gray white massive tiny defect (MQ), a pattern of gray black massive tiny defect (MN), a pattern of gray black strip defect (MR), a pattern of black halo defect (MT), a pattern of bubble-shaped large defect (WN), a pattern of false count (FC) and a pattern of other defect (OT); wherein a plurality of features of said second defect of each said defect pattern and the same features of said first defect of each said defect image are obtained; and said features are a plurality of color features, shape features and statistical features; and wherein said features of said first defect of each said defect image are compared with the same features of said second defect of each said defect pattern to obtain a separate similarity to each said defect pattern to categorize said defect image into at least one of said defect patterns; and (c) defect classification, wherein said features of said second defects of all said defect patterns are processed through a classification and regression tree (CART) algorithm to obtain classification rules with Gini indexes to automatically classify said defect images; a result of classified defect images obtained in step (c) is compared with a result of categorized defect images obtained in step (b); and a defect image is classified into said pattern of OT when a defect pattern into which said defect image is classified in step (c) is not identical with any defect pattern into which said defect image is categorized in step (b).