Analysis of comparative method for association rule mining
Summary :
Table of Contents
- Abstract
- Introduction
- Data mining
- Association rules and frequent item sets
- The A-priori algorithm
- Algorithm (FP-tree construction)
- Recent research in the field
- Conclusion
- Bibliography
Abstract
association rule mining finds interesting associations and/or correlation relationships among large set of data items. association rules shows attribute value conditions that occur frequently together in a given data set. association rules provide information of this type in the form of "if-then" statements. These rules are computed from the data and, unlike the if-then rules of logic, association rules are probabilistic in nature. mining frequent patterns in transaction databases, time series databases adopt an Apriori like candidate generation-and test approach. However, candidate set generation is costly, especially when there exist a large number of patterns and/or long patterns. Frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed information about frequent patterns in transactional database, is there to find the frequent pattern base from the database.
Keywords: Frequent pattern, FP tree, association rule mining
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