Staffordshire University logo
STORE - Staffordshire Online Repository

FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams

Garcıa-Vico, Angel M., Carmona, Cristobal, Gonzalez, Pedro, SEKER, Huseyin and del Jesus, Maria (2020) FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams. IEEE Transactions on Fuzzy Systems. ISSN 1063-6706

[img]
Preview
Text
IEEE Fuzzy Systems May2020 Accepted Version.pdf - AUTHOR'S ACCEPTED Version (default)
Available under License All Rights Reserved (Under Embargo).

Download (374kB) | Preview

Abstract or description

Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorised as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns are interesting in a data stream context as easy, fast, reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory and continuous model updates.

In this paper, an approach for the extraction of emerging patterns in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential.

Item Type: Article
Additional Information: “© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords: Data mining, Adaptation models, Data models, Monitoring, Predictive models, Proposals, Fuzzy systems
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Huseyin SEKER
Date Deposited: 14 Jul 2020 14:31
Last Modified: 01 Sep 2020 10:16
URI: http://eprints.staffs.ac.uk/id/eprint/6429

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000