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Abstract
Abstract
Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.
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Grislin-Le Strugeon E, Marcal de Oliveira K, Thilliez M, Petit D. A systematic mapping study on agent mining. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2020.1864784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | | | - Marie Thilliez
- Univ. Polytechnique Hauts-de-France, LAMIH - UMR CNRS, Valenciennes, France
| | - Dorian Petit
- Univ. Polytechnique Hauts-de-France, LAMIH - UMR CNRS, Valenciennes, France
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Lorpunmanee S, Kamonsantiroj S. Efficient Mining Recurring Patterns of Inter-Transaction in Time Series. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019. [DOI: 10.20965/jaciii.2019.p0402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
One type of the partial periodic pattern is known as recurring patterns, which exhibit cyclic repetitions only for particular time period within a series. A key property of the patterns is the event can start, stop, and restart at anytime within a series. Therefore, the extracted meaningful knowledge from the patterns is challenging because the information can vary across patterns. The mining technique in recurring patterns plays an important role for discovering knowledge pertaining to seasonal or temporal associations between events. Most existing researches focus on discovering the recurring patterns in transaction. However, these researches for mining recurring patterns cannot discover recurring events across multiple transactions (inter-transaction) which often appears in many real-world applications such as the stock exchange market, social network, etc. In this study, the proposed algorithm, namely, CP-growth can efficiently perform in discovering the recurring patterns within inter-transaction. Besides, an efficient pruning technique to reduce the computational cost of discovering recurring patterns is developed in CP-growth algorithm. Experimental results show that recurring patterns can be useful in multiple transactions and the proposed algorithm, namely, CP-growth is efficient.
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