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An Approach to Mining Crime Patterns
Abstract
This paper presents a knowledge discovery effort to retrieve meaningful information about crime from a U.S. state database. The raw data were preprocessed, and data cubes were created using Structured Query Language (SQL). The data cubes then were used in deriving quantitative generalizations and for further analysis of the data. An entropy-based attribute relevance study was undertaken to determine the relevant attributes. A machine learning software called WEKA was used for mining association rules, developing a decision tree, and clustering. SOM was used to view multidimensional clusters on a regular two-dimensional grid.
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