Research and application of fuzzy association rules algorithm

The intrusion detection technology based on data mining is an emerging and promising security measure in current researches. In order to solve problems existing in current algorithm, this paper proposes a new improved fuzzy association rules algorithm that integrates Apriori and Kuok's algorithm. This improved algorithm, by introducing fuzzy membership function, decision tree scheme, similarities of set of rules, employed these three structures to build rules for quantitative attributes. It optimized a number of problems that exist in applying association rules algorithm to intrusion detection: redundancy data for scanning and unwanted frequent set produced in the old two-phrase rule-building method. Experimental results have demonstrated the algorithm's particular better performance in both rule building efficacy and time efficiency.