A Novel Approach for Mining Similarity Profiled Temporal Association Patterns

TL;DR

This paper introduces a novel single-scan method for mining similar temporal association patterns in temporal databases, utilizing positive and negative supports and Venn diagrams to improve efficiency over traditional multi-scan algorithms.

Contribution

It proposes a new approach that efficiently finds similar temporal patterns with a single database scan, reducing computational overhead and complexity.

Findings

Performs only one scan of the temporal database

Eliminates the need to compute supports of all subsets

Reduces processing time compared to traditional methods

Abstract

The problem of frequent pattern mining from non-temporal databases is studied extensively by various researchers working in areas of data mining, temporal databases and information retrieval. However, Conventional frequent pattern algorithms are not suitable to find similar temporal association patterns from temporal databases. A Temporal database is a database which can store past, present and future information. The objective of this research is to come up with a novel approach so as to find similar temporal association patterns w.r.t user specified threshold and a given reference support time sequence using concept of Venn diagrams. For this, we maintain two types of supports called positive support and negative support values to find similar temporal association patterns of user interest. The main advantage of our method is that, it performs only a single scan of temporal database to find temporal association patterns similar to specified reference support sequence. This single database scan approach hence eliminates the huge overhead incurred when the database is scanned multiple times. The present approach also eliminates the need to compute and maintain true support values of all the subsets of temporal patterns of previous stages when computing temporal patterns of next stage.