A Data Mining Approach For Non-Redundant Frequent Patterns
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In today's electronic world vast amounts of knowledge is stored within many datasets and databases. These datasets or databases contain huge amount of hidden patterns or knowledge. Often these patterns or knowledge is not immediately accessible, but rather has to be mined or extracted. This requires Knowledge discovery techniques and tools and they need to be effective and efficient. Association rule mining is one of the Knowledge discovery techniques, used to obtain the pattern based knowledge stored in datasets or databases. These frequent patterns and Association rules exist between the ite...
In today's electronic world vast amounts of knowledge is stored within many datasets and databases. These datasets or databases contain huge amount of hidden patterns or knowledge. Often these patterns or knowledge is not immediately accessible, but rather has to be mined or extracted. This requires Knowledge discovery techniques and tools and they need to be effective and efficient. Association rule mining is one of the Knowledge discovery techniques, used to obtain the pattern based knowledge stored in datasets or databases. These frequent patterns and Association rules exist between the items or attributes of a datasets. However, the Association rule mining's extracted number of rules can be very big. In order to effectively use the Association rules and the knowledge or information within, the rules needs to be kept manageable. Thus it is necessary to have a method to reduce the number of Association rules. However, there is no loss of knowledge or information through this process.
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