New approach in data stream association rule mining based on graph structure

Authors:
Samad Gahderi Mojaveri;Esmaeil Mirzaeian;Zarrintaj Bornaee;Saeed Ayat
Affiliations:
Payame Noor University, Tehran, Iran;Payame Noor University, Tehran, Iran;Iranian Research Organization for Science & Technology, Tehran, Iran;Payame Noor University, Iran
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

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Abstract

Discovery of useful information and valuable knowledge from transactions has attracted many researchers due to increasing use of very large databases and data warehouses. Furthermore most of proposed methods are designed to work on traditional databases in which re-scanning the transactions is allowed. These methods are not useful for mining in data streams (DS) because it is not possible to re-scan the transactions duo to huge and continues data in DS. In this paper, we proposed an effective approach to mining frequent itemsets used for association rule mining in DS named GRM1. Unlike other semi-graph methods, our method is based on graph structure and has the ability to maintain and update the graph in one pass of transactions. In this method data storing is optimized by memory usage criteria and mining the rules is done in a linear processing time. Efficiency of our implemented method is compared with other proposed method and the result is presented.