A Time-Interval Sequential Pattern Change Detection Method

Several studies have focused on mining changes in different time-period databases. Analyzing these change behaviors provides useful information for managers to develop better marketing strategies and decision making. Although some researchers have developed efficient methods for association rule change detection, no attempt has been made to analyze time-interval sequential pattern changes in databases collected over time. Therefore, this research proposes a time-interval sequential pattern change detection framework to derive the change trends in customer behaviors in two periods. First, two time-interval sequential pattern sets are generated from two time-period databases respectively using the proposedDTI-Apriorialgorithm. Different from previous mining methods that require users to manually define a set of time-interval ranges in advance, theDTI-Apriorialgorithm automatically arranges the time-interval range and then generates time-interval sequential patterns. The degree of change for each pair of time-interval sequential patterns from different time periods is evaluated next. Based on the degree of change, a time-interval sequential pattern is clarified as one of the following three change types: an emerging time-interval sequential pattern, an unexpected time-interval sequential pattern, or an added/perished time-interval sequential pattern. Significant change patterns are returned to users for further analysis if the degree of change is large enough.