Mining Fuzzy Association Rules from Web Usage Quantitative Data

Abstract

Web usage mining is the method of extracting interesting patterns from Web usage log file. Web usage mining is subfield of data mining uses various data mining techniques to produce association rules. Data mining techniques are used to generate association rules from transaction data. Most of the time transactions are boolean transactions, whereas Web usage data consists of quantitative values. To handle these real world quantitative data we used fuzzy data mining algorithm for extraction of association rules from quantitative Web log file. To generate fuzzy association rules first we designed membership function. This membership function is used to transform quantitative values into fuzzy terms. Experiments are carried out on different support and confidence. Experimental results show the performance of the algorithm with varied supports and confidence.