الفهرس 
DATA MINING: An OverviewOnly 14 pages are availabe for public view
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Abstract
ABSTRACT Recently, our capabilities of both generating and collecting data have been increasing rapidly. The wide spread use of bar codes for most commercial products, the computerization of many businesses and government transactions, and the advances in data collection tools has provided us with huge amounts of data. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. knowledge discovery (or Data mining) systems are software systems extract nontrivial information (such as knowledge rules, constraints, and regularities), implied in data, previously unknown and potentially useful from data in databases. Sometimes, the term data mining is used to describe algorithms and methods that extract special types of patterns (such that Association Rules, Characteristic Rules, Discriminant Rules, Classification Rules, Clustering, Evolution, Deviations) from databases which is the central part of knowledge discovery process. But the term knowledge discovery is used to describe a collection of many processes, including, data collection, data preparation, data cleaning, data mining, and output representation. The discovered knowledge can be applied to information management, query processing, decision making, process control, and several emerging applications in information providing services, such as on-line services and World Wide Web. One of the most important knowledge extracted from database is the association rules. An example of association rules the statement: “90% of transactions that purchase bread and butter also purchase milk ”. This association rule is represented in the form: bread, butter ? milk | 90%. The antecedent of this rule consists of bread and butter and the consequent consists of milk alone. The number 90% is the confidence factor of the rule.Specifically, given a database of sales transactions, one would like to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction.