Analysis of comparative method for association rule mining

Type :

Term papers

Pages :

4 pages

Format :

.pdf

Published date :

09/21/2009

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Summary :

 
 

Table of Contents Analysis of comparative method for association rule mining Table of Contents

 
  1. Abstract
  2. Introduction
    1. Data mining
    2. Association rules and frequent item sets
    3. The A-priori algorithm
    4. Algorithm (FP-tree construction)
  3. Recent research in the field
  4. Conclusion
  5. 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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About the author :

pencil image Vijay P. Director of Shri HDG MBA College
Level :General public Study : Management School/University : Saurashtra University

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