数据挖掘介绍28761.doc

PART ONE INTRODUCTION CHAPTER 1 Introduction 1.1 BASIC DATA MINING TASKS 1.2 DATA MINING VERSUS KNOWLEDGE DISCOVERY IN DATABASES 1.3 DATA MINING ISSUES 1.4 DATA MINING METRICS 1.5 SOCIAL IMPLICATIONS OF DATA MINING 1.6 DATA MINING FROM A DATABASE PERSPECTIVE 1.7 THE FUTURE 1.8 EXERCISES 1.9 BIBLIOGRAPHIC NOTES The amount of data kept in computer files and databases is growing at a phenomenal rate. At the same time, the users of these data are expecting more sophisticated information from them. A marketing manager is no longer satisfied with a simple listing of marketing contacts, but wants detailed information about customers’ past purchases as well as predictions of future purchases. Simple structured/query language queries are not adequate to support these increased demands for information. Data mining steps in to solve these needs. Data mining is often defined as finding hidden information in a database. Alternatively, it has been called exploratory data analysis data driven discovery, and inductive learning. Traditional database queries (Figure 1.1), access a database using a well-defined query stated in a language such as SQL. The output of the query consists of the data from the database that satisfies the query. The output is usually a subset of the database, but it may also be an extracted view or may contain aggregations. Data mining access of a database differs from this traditional access in severaL ways: ?Query: The query might not be well formed or precisely stated. The data miner might not even be exactly sure of what he wants to see. ?Data: The data accessed is usually a different version from that of the original operational database. The data have been cleansed and modified to better support the mining process. ?Output: The output of the data mining query probably is not a subset of the database, Instead it is the output of some analysis of the contents of the database. The current state of the art of data mining is similar to that of database que