A sliding windows based dual support framework for discovering emerging trends from temporal data

Khan, M. Sulaiman and Coenen, F. and Reid, D. and Patel, R. and Archer, L. (2010) A sliding windows based dual support framework for discovering emerging trends from temporal data. Knowledge-Based Systems, 23 (4). pp. 316-322. ISSN 0950-7051

Full text not available from this repository. (Request a copy)

Abstract

In this paper we present the dual support Apriori for temporal data (DSAT) algorithm. This is a novel technique for discovering jumping and emerging patterns (JEPs) from time series data using a sliding window technique. Our approach is particularly effective when performing trend analysis in order to explore the itemset variations over time. Our proposed framework is different from the previous work on JEP in that we do not rely on itemsets borders with a constrained search space. DSAT exploits previously mined time stamped data by using a sliding window concept, thus requiring less memory, minimum computational cost and very low dataset accesses. DSAT discovers all JEPs, as in “naïve” approaches, but utilises less memory and scales linearly with large datasets sets as demonstrated in the experimental section.

Item Type: Article
Additional Information and Comments: NOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systemsy,23(4), 2010 DOI: doi:10.1016/j.knosys.2009.11.005
Keywords: Association rule mining, Jumping emerging patterns, Temporal trends, Time series, Sliding window
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department: Faculty of Human and Digital Sciences > School of Computer Science and the Environment
Depositing User: Users 4 not found.
Date Deposited: 10 Mar 2014 08:46
Last Modified: 22 Jan 2025 11:41
URI: https://hira.hope.ac.uk/id/eprint/68

Actions (login required)

View Item View Item