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What is Data Mining?Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases”, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery consists of an iterative sequence of the following steps: · data cleaning: to remove noise or irrelevant data, · data integration: where multiple data sources may be combined,· data selection : where data relevant to the analysis task are retrieved from the database,· data transformation : where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance,· data mining: an essential process where intelligent methods are applied in order to extract data patterns, · pattern evaluation: to identify the truly interesting patterns representing knowledge based on some interestingness measures, and · knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user . The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. Note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers hidden patterns for evaluation. We agree that data mining is a knowledge discovery process. However, in industry, in media, and in the database research milieu, the term “data mining” is becoming more popular than the longer term of “knowledge discovery in databases”. Therefore, in this book, we choose to use the term “data mining”. We adopt a broad view of data mining functionality: data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Based on this view, the architecture of a ty