Efficient mining fuzzy association rules from ubiquitous data streams

Abstract

Due to the development in technology, a number of applications such as smart mobile phone, sensor networks and GPS devices produce huge amount of ubiquitous data in the form of streams. Different from data in traditional static databases, ubiquitous data streams typically arrive continuously in high speed with huge amount, and changing data distribution. Dealing with and extracting useful information from that data is a real challenge. This raises new issues, that need to be considered when developing association rule mining techniques for these data. It should be noted, that data, in the real world, are not represented in binary and numeric forms only, but it may be represented in quantitative values. Thus, using fuzzy sets will be very suitable to handle these values.In this paper the problem of mining fuzzy association rules from ubiquitous data streams is studied, and a novel technique FFP_USTREAM (Fuzzy Frequent Pattern Ubiquitous Streams) is developed. This technique integrates fuzzy concepts with ubiquitous data streams, employing sliding window approach, to mine fuzzy association rules. In addition, the complexity and the efficiency of this technique are discussed. Examples of real data sets are used to test the proposed technique. Further research issues are also suggested.