数据挖掘导论第一章.pptVIP

* 数据挖掘导论 * 关联规则:应用2 Supermarket shelf management. Goal: To identify items that are bought together by sufficiently many customers. Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule -- If a customer buys diaper and milk, then he is very likely to buy beer. So, don’t be surprised if you find six-packs stacked next to diapers! * 数据挖掘导论 * 聚类: 定义 Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that Data points in one cluster are more similar to one another. Data points in separate clusters are less similar to one another. Similarity Measures: Euclidean Distance if attributes are continuous. Other Problem-specific Measures Intracluster distances are minimized Intercluster distances are maximized * 数据挖掘导论 * 聚类: 应用1 Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Approach: Collect different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. * 数据挖掘导论 * 聚类: 应用2 Document Clustering: Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents * 数据挖掘导论 * 文档聚类: 例 Clustering Points: 3204 Articles of Los Angeles Times. Similarity Measure: How many words are common in these documents (after some word filtering). * 数据挖掘导论 * 异常检测 任务：识别其特征显著不同于其他数据的观测值 这样的观测值称为异常点（anomaly）或离群点（outlie