Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets

Traditional methods for data mining typically make the assumption that data is centralized and static. This assumption is no longer tenable. Such methods waste computational and I/O resources when data is dynamic, and they impose excessive communication overhead when data is distributed. As a result, the knowledge discovery process is harmed by slow response times. Efficient implementation of incremental data mining ideas in distributed computing environments is thus becoming crucial for ensuring scalability and facilitate knowledge discovery when data is dynamic and distributed. In this paper we address this issue in the context of frequent itemset mining, an important data mining task. Frequent itemsets are most often used to generate correlations and association rules, but more recently they have also been used in such far-reaching domains as bio-informatics and e-commerce applications. We first present an efficient algorithm which dynamically maintains the required information e...

• Type: Conference
• Year: 2003
• Where: HIPC
• Authors: Adriano Veloso, Matthew Eric Otey, Srinivasan Parthasarathy, Wagner Meira Jr.
• Keywords: Data Mining, Frequent Itemsets, HIPC 2003, Incremental Data Mining