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Data Mining Concepts
Over the last three decades, many organizations have generated a large amount of machine-readable data in the form of files and databases. To process this data, we have the database technology available that supports query languages like SQL. The problem with SQL is that it is a structured language that assumes the user is aware of the database schema. SQL supports operations of relational algebra that allow a user to select rows and columns of data from tables or join-related information from tables based on common fields. In the next chapter, we will see that data warehousing technology affords several types of functionality: that of consolidation, aggregation, and summarization of data. Data warehouses let us view the same information along multiple dimensions. In this chapter, we will focus our attention on another very popular area of interest known as data mining. As the term connotes, data mining refers to the mining or discovery of new information in terms of patterns or rules from vast amounts of data. To be practically useful, data mining must be carried out efficiently on large files and databases. Although some data mining features are being provided in RDBMSs, data mining is not well-integrated with database management systems.
We will briefly review the state of the art of this rather extensive field of data mining, which uses techniques from such areas as machine learning, statistics, neural networks, and genetic algorithms. We will highlight the nature of the information that is discovered, the types of problems faced when trying to mine databases, and the types of applications of data mining. We will also survey the state of the art of a large number of commercial tools available (see Section 28.7) and describe a number of research advances that are needed to make this area viable.
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