Efficient Association Rules Mining from Streaming Data with a Fault Tolerance

Recently, number of applications including social networks, stock market trading and sensor network devices generate a massive amount of data in the streaming form. Streaming data have characteristics different from static data, such as streaming data arrives continuously at high speed with huge amount. Mining and discovering information from these data is a non-trivial issue. Most of traditional algorithms have limitations to deal with streaming data, so there are new issues raised and need to be taken into consideration while developing techniques for mining association rules from such data. In this paper, a technique to mine an association rules from streaming data efficiently is proposed. The proposed technique develops a tree structure called Fast Update Frequent Pattern Tree (FUFP-Tree) that reduce the number of traversing between tree nodes in both inserting a new transaction and extracting an association rules between items. Also, to avoid congestion during inserting incoming streaming data to FUFP-Tree, a sliding window approach is used to divide incoming data equally to all available windows. The complexity and the performance of this technique are investigated, and a dataset of storehouse is used to test the proposed technique and measure its efficiency. The efficiency of the proposed technique is compared with other most related algorithms.