Data Mining: Concepts and Techniques, Second Edition (The by Jiawei Han

By Jiawei Han

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Extra info for Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)

Example text

For example, we may like to detect intrusions of a computer network based on the anomaly of message flow, which may be discovered by clustering data streams, dynamic construction of stream models, or comparing the current frequent patterns with that at a certain previous time. Most stream data reside at a rather low level of abstraction, whereas analysts are often more interested in higher and multiple levels of abstraction. Thus, multilevel, multidimensional on-line analysis and mining should be performed on stream data as well.

Each school may have its own computer system and use its own curriculum and grading system. One university may adopt a quarter system, offer three courses on database systems, and assign grades from A+ to F, whereas another may adopt a semester system, offer two courses on databases, and assign grades from 1 to 10. It is very difficult to work out precise course-to-grade transformation rules between the two universities, making information exchange difficult. Data mining techniques may provide an interesting solution to the information exchange problem by performing statistical data distribution and correlation analysis, and transforming the given data into higher, more generalized, conceptual levels (such as fair, good, or excellent for student grades), from which information exchange can then more easily be performed.

For instance, the mining of banking data may aid in the scheduling of bank tellers according to the volume of customer traffic. ). Such analyses typically require defining multiple granularity of time. For example, time may be decomposed according to fiscal years, academic years, or calendar years. Years may be further decomposed into quarters or months. Spatial Databases and Spatiotemporal Databases Spatial databases contain spatial-related information. Examples include geographic (map) databases, very large-scale integration (VLSI) or computed-aided design databases, and medical and satellite image databases.

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