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 GRM. 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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mojaveri, S. G., Mirzaeian, E., Bornaee, Z., & Ayat, S. (2010). New approach in data stream association rule mining based on graph structure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6171 LNAI, pp. 158–164). https://doi.org/10.1007/978-3-642-14400-4_12
Mendeley helps you to discover research relevant for your work.