Discovery of Periodic Rare Correlated Patterns from Static Database

Upadhya K. Jyothi; B. Dinesh Rao; M. Geetha; Harsh Kamlesh Vora

doi:10.1007/978-981-19-2225-1_56

Discovery of Periodic Rare Correlated Patterns from Static Database

Upadhya K. Jyothi^*, B. Dinesh Rao, M. Geetha, Harsh Kamlesh Vora

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called PRCPMiner is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.

Original language	English
Title of host publication	Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021
Editors	Bibudhendu Pati, Chhabi Rani Panigrahi, Prasant Mohapatra, Kuan-Ching Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	649-660
Number of pages	12
ISBN (Print)	9789811922244
DOIs	https://doi.org/10.1007/978-981-19-2225-1_56
Publication status	Published - 2023
Event	6th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2021 - Bhubaneswar, India Duration: 23-12-2021 → 24-12-2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	428
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2021
Country/Territory	India
City	Bhubaneswar
Period	23-12-21 → 24-12-21

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-19-2225-1_56

Cite this

Jyothi, U. K., Rao, B. D., Geetha, M., & Vora, H. K. (2023). Discovery of Periodic Rare Correlated Patterns from Static Database. In B. Pati, C. R. Panigrahi, P. Mohapatra, & K.-C. Li (Eds.), Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021 (pp. 649-660). (Lecture Notes in Networks and Systems; Vol. 428). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-2225-1_56

Jyothi, Upadhya K. ; Rao, B. Dinesh ; Geetha, M. et al. / Discovery of Periodic Rare Correlated Patterns from Static Database. Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021. editor / Bibudhendu Pati ; Chhabi Rani Panigrahi ; Prasant Mohapatra ; Kuan-Ching Li. Springer Science and Business Media Deutschland GmbH, 2023. pp. 649-660 (Lecture Notes in Networks and Systems).

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title = "Discovery of Periodic Rare Correlated Patterns from Static Database",

abstract = "Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called PRCPMiner is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.",

author = "Jyothi, {Upadhya K.} and Rao, {B. Dinesh} and M. Geetha and Vora, {Harsh Kamlesh}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2021 ; Conference date: 23-12-2021 Through 24-12-2021",

year = "2023",

doi = "10.1007/978-981-19-2225-1_56",

language = "English",

isbn = "9789811922244",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "649--660",

editor = "Bibudhendu Pati and Panigrahi, {Chhabi Rani} and Prasant Mohapatra and Kuan-Ching Li",

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Jyothi, UK, Rao, BD, Geetha, M & Vora, HK 2023, Discovery of Periodic Rare Correlated Patterns from Static Database. in B Pati, CR Panigrahi, P Mohapatra & K-C Li (eds), Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021. Lecture Notes in Networks and Systems, vol. 428, Springer Science and Business Media Deutschland GmbH, pp. 649-660, 6th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2021, Bhubaneswar, India, 23-12-21. https://doi.org/10.1007/978-981-19-2225-1_56

Discovery of Periodic Rare Correlated Patterns from Static Database. / Jyothi, Upadhya K.; Rao, B. Dinesh; Geetha, M. et al.
Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021. ed. / Bibudhendu Pati; Chhabi Rani Panigrahi; Prasant Mohapatra; Kuan-Ching Li. Springer Science and Business Media Deutschland GmbH, 2023. p. 649-660 (Lecture Notes in Networks and Systems; Vol. 428).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Discovery of Periodic Rare Correlated Patterns from Static Database

AU - Jyothi, Upadhya K.

AU - Rao, B. Dinesh

AU - Geetha, M.

AU - Vora, Harsh Kamlesh

PY - 2023

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N2 - Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called PRCPMiner is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.

AB - Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called PRCPMiner is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.

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BT - Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021

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ER -

Jyothi UK, Rao BD, Geetha M, Vora HK. Discovery of Periodic Rare Correlated Patterns from Static Database. In Pati B, Panigrahi CR, Mohapatra P, Li KC, editors, Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering - ICACIE 2021. Springer Science and Business Media Deutschland GmbH. 2023. p. 649-660. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-2225-1_56

Discovery of Periodic Rare Correlated Patterns from Static Database

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