# A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Authors
• 1 VNR Vignana Jyothi Institute of Engineering and Technology, Department of IT, Hyderabad, India , Hyderabad (India)
• 2 Jordan University of Science and Technology, Department of Software Engineering, Irbid, Jordan , Irbid (Jordan)
• 3 University College of Engineering, Osmania University, Department of CSE, Hyderabad, India , Hyderabad (India)
• 4 University of Texas at San Antonio, Department of Information Systems and Cyber Security, San Antonio, TX, USA , San Antonio (United States)
• 5 University of South Australia, School of Information Technology and Mathematical Sciences, Adelaide, Australia , Adelaide (Australia)
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Nov 18, 2016
Volume
22
Issue
6
Pages
1903–1919
Identifiers
DOI: 10.1007/s00500-016-2445-y
Source
Springer Nature
Keywords
Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing 2n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2^{n }$$\end{document} true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.