An Efficient Single-Pass Algorithm for Mining Association Rules from Wireless Sensor Networks

摘要

With advances in technology, use of wireless sensor networks (WSNs) has widely increased in recent decades. In general, WSNs produce a large amount of data in the form of streams. Recently, data-mining techniques have received a great deal of attention for their utility in extracting knowledge from WSN data. Mining association rules on the sensor data provides useful information for different applications. Even though there have been some efforts to address this issue in WSNs, they are not suitable when the user might not have enough opportunity to scan the database multiple times, which is highly common in the WSN environment. Therefore, in this paper we propose a new tree-based data structure called sensor pattern tree (SP-tree) to generate the set of all association rules from WSN data with one scan over the sensor database. The SP-tree is constructed in frequency-descending order, which facilitates an efficient mining using the frequent pattern (FP)-growth-based mining technique. Our experimental results show that SP-tree outperforms related algorithms in finding association rules from WSN data.