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Efficient Depth-First Search Approach for Mining Injective General Episodes

Gandreti, SB and Sastry, PS (2023) Efficient Depth-First Search Approach for Mining Injective General Episodes. In: 6th ACM India Joint International Conference on Data Science and Management of Data, CODS-COMAD 2023, 4 January - 7 January 2023, Mumbai, pp. 1-9.

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Official URL: https://doi.org/10.1145/3570991.3571012


Frequent episode mining is useful for finding temporal patterns in sequential data. Episodes represent partially ordered sets of event-types and frequently occurring episodes can capture temporal dependencies in the data. There are many algorithms in the literature that find a subset of frequent episodes that best summarize the data using serial episodes (which are episodes with total order). But there are no such algorithms for the case of general episodes. An efficient Depth-First search (DFS) approach for mining general episodes would be a crucial tool and a necessary first step for generating a subset of general episodes that best represent the given temporal data. In this paper, we present an efficient algorithm to mine for general injective episodes in a DFS manner. Our algorithm uses both the apriori principle and the idea of bidirectional evidence to prune the search space and it returns closed frequent episodes. Through simulation studies, we show that the algorithm is quite effective and efficient when compared with the existing algorithms.

Item Type: Conference Paper
Publication: ACM International Conference Proceeding Series
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to Association for Computing Machinery
Keywords: Data mining, Depth first; Depth-first search; Depth-first search approaches; Episode mining; Frequent episode minings; Frequent episodes; Injective episode; Partial order; Sequential data; Temporal pattern, Set theory
Department/Centre: Division of Mechanical Sciences > Mechanical Engineering
Date Deposited: 07 Feb 2023 07:33
Last Modified: 07 Feb 2023 07:33
URI: https://eprints.iisc.ac.in/id/eprint/80110

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