Mining Outlier Temporal Association Patterns

Temporal pattern mining is the recent research among researchers contributing in the areas of data mining, medical mining, spatial data mining, health informatics and gaining significant interest in Internet of things, one of the top ten fields of interest expected to rule the computing world in the next few years. Temporal pattern mining is a knowledge discovery process which concentrates on mining temporal databases for discovering hidden temporal information which includes finding temporal patterns, temporal and spatio- temporal association rules, performing temporal clustering and classification to name a few of them. In this paper, the major objective is to find temporally outlier patterns from the temporal database of disjoint time-stamped transactions in a single database scan without a need to scan the database multiple times. We aim to eliminate n-1 database scans, performed when we use conventional approach of finding temporal patterns. We demonstrate the approach using a case study. The results show that the proposed approach is computationally efficient which is essentially because of single scan performed.