A dissimilarity measure for mining similar temporal association patterns

This research address the design of a new dissimilarity measure and applying it to find all valid similarity profiled patterns in a temporal database defined over finite number of time slots. The proposed dissimilarity measure is a function of the reference sequence, threshold and standard deviation. Given, a reference time sequence and allowable dissimilarity limit, unearthing all eccentric (similar) temporal association patterns requires a similarity or correlation measure that can estimate similar association patterns accurately, efficiently, and is computationally optimal. This research also proposes a method to estimate temporal pattern support bounds. The experiment result shows the advantage of our proposed measure and bound estimation approach and also proves that our method is computationally efficient when compared to naïve, sequential and Spamine approaches.