Ø Broadly speaking, data mining is the process of semi-automatically analyzing large databases to find useful patterns.
Ø Like knowledge discovery in artificial intelligence data mining discovers statistical rules and patterns
Ø Differs from machine learning in that it deals with large volumes of data stored primarily on disk.
Ø Some types of knowledge discovered from a database can be represented by a set of rules. e.g.,: ―Young women with annual incomes greater than $50,000 are most likely to buy sports cars‖.
Ø Other types of knowledge represented by equations, or by prediction functions.
Ø Some manual intervention is usually required
· Pre-processing of data, choice of which type of pattern to find, postprocessing to find novel patterns
Applications of Data Mining
Ø Prediction based on past history
· Predict if a credit card applicant poses a good credit risk, based on some attributes (income, job type, age, ..) and past history
· Predict if a customer is likely to switch brand loyalty
· Predict if a customer is likely to respond to ―junk mail‖
· Predict if a pattern of phone calling card usage is likely to be fraudulent Ø Some examples of prediction mechanisms:
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