A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams

Stahl, Frederic and Le, Thien and Badii, Atta and Gaber, Mohamed Medhat (2021) A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams. Information, 12 (1). e24. ISSN 2078-2489

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This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of `explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. The paper also provides a theoretical, qualitative and empirical evaluation of FGRI.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** History: accepted 02-01-2021; pub-electronic 09-01-2021. ** Licence for this article: https://creativecommons.org/licenses/by/4.0/
Identification Number: https://doi.org/10.3390/info12010024
2 January 2021Accepted
9 January 2021Published
Uncontrolled Keywords: data stream mining, generalised rule induction, concept drift
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
SWORD Depositor: JISC PubRouter
Depositing User: JISC PubRouter
Date Deposited: 12 Jan 2021 09:54
Last Modified: 12 Jan 2022 12:53
URI: https://www.open-access.bcu.ac.uk/id/eprint/10720

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