Binary prediction based on weighted sequential mining method

Abstract

This paper presents a weighted-binary-sequential method to predict the status of customer patronage for the next day. Most of the research using association rules to mine sequential data focus on the algorithms and computing efficiency of pattern or rule generation. But few of them consider the time value of the sequential data. It is desirable to weight recent observations more heavily than remote observations in the analysis of time-series data. In this paper, we address a time-weighted concept on association algorithm to mine the binary-time-series data. The weighted binary sequence algorithm gives more weight on the recent data in finding the longest frequent patterns from binary-time-series data. There are two weighting methods; dynamic-length weighting and fixed-length weighting. Both algorithms are compared to the un-weighted algorithm to show how time value influences the prediction accuracy. Some performance results with a real-life Web site application given in this paper show that time-weighted sequential algorithms are generally superior to un-weighted sequential algorithm.