Efficient Mining of Rare Itemsets

Shwetha Rai; Geetha M; Preetham Kumar

doi:10.1007/978-981-33-4909-4_43

Efficient Mining of Rare Itemsets

Shwetha Rai^*, Geetha M, Preetham Kumar

^*Corresponding author for this work

Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal
Department of Information and Communication Technology, Manipal Institute of Technology, Manipal

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

Abstract

Itemset mining discovers interesting patterns in the dataset. The itemset may be frequent or it can be rare based on its occurrence in the database. It has been observed that most of the algorithms are designed for mining frequent itemsets. However, discovery of rare itemsets is equally important since they play a major role in making decisions in some situations. The efficiency of the algorithms depend on the way in which the data structures are designed to store and retrieve the data. Hyperlinked Rare Pattern Mining algorithm discovers all rare itemsets and is suitable for sparse dataset. In this algorithm item_id and its support count are stored in Support and Header tables. This redundancy is removed in the proposed algorithm to improve the time efficiency. An experimental analysis is conducted to discover the rare itemsets. It is observed that while there is an improvement in time efficiency, there is a tradeoff for space efficiency.

Original language	English
Title of host publication	International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020
Editors	V. Bindhu, João Manuel Tavares, Alexandros-Apostolos A. Boulogeorgos, Chandrasekar Vuppalapati
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	569-577
Number of pages	9
ISBN (Print)	9789813349087
DOIs	https://doi.org/10.1007/978-981-33-4909-4_43
Publication status	Published - 2021
Event	International Conference on Communication, Computing and Electronics Systems, ICCCES 2020 - Coimbatore, India Duration: 21-10-2020 → 22-10-2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	733 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Communication, Computing and Electronics Systems, ICCCES 2020
Country/Territory	India
City	Coimbatore
Period	21-10-20 → 22-10-20

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-33-4909-4_43

Cite this

Rai, S., M, G., & Kumar, P. (2021). Efficient Mining of Rare Itemsets. In V. Bindhu, J. M. Tavares, A.-A. A. Boulogeorgos, & C. Vuppalapati (Eds.), International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020 (pp. 569-577). (Lecture Notes in Electrical Engineering; Vol. 733 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-4909-4_43

Rai, Shwetha ; M, Geetha ; Kumar, Preetham. / Efficient Mining of Rare Itemsets. International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020. editor / V. Bindhu ; João Manuel Tavares ; Alexandros-Apostolos A. Boulogeorgos ; Chandrasekar Vuppalapati. Springer Science and Business Media Deutschland GmbH, 2021. pp. 569-577 (Lecture Notes in Electrical Engineering).

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title = "Efficient Mining of Rare Itemsets",

abstract = "Itemset mining discovers interesting patterns in the dataset. The itemset may be frequent or it can be rare based on its occurrence in the database. It has been observed that most of the algorithms are designed for mining frequent itemsets. However, discovery of rare itemsets is equally important since they play a major role in making decisions in some situations. The efficiency of the algorithms depend on the way in which the data structures are designed to store and retrieve the data. Hyperlinked Rare Pattern Mining algorithm discovers all rare itemsets and is suitable for sparse dataset. In this algorithm item_id and its support count are stored in Support and Header tables. This redundancy is removed in the proposed algorithm to improve the time efficiency. An experimental analysis is conducted to discover the rare itemsets. It is observed that while there is an improvement in time efficiency, there is a tradeoff for space efficiency.",

author = "Shwetha Rai and Geetha M and Preetham Kumar",

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Rai, S , M, G & Kumar, P 2021, Efficient Mining of Rare Itemsets. in V Bindhu, JM Tavares, A-AA Boulogeorgos & C Vuppalapati (eds), International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020. Lecture Notes in Electrical Engineering, vol. 733 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 569-577, International Conference on Communication, Computing and Electronics Systems, ICCCES 2020, Coimbatore, India, 21-10-20. https://doi.org/10.1007/978-981-33-4909-4_43

Efficient Mining of Rare Itemsets. / Rai, Shwetha ; M, Geetha ; Kumar, Preetham.
International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020. ed. / V. Bindhu; João Manuel Tavares; Alexandros-Apostolos A. Boulogeorgos; Chandrasekar Vuppalapati. Springer Science and Business Media Deutschland GmbH, 2021. p. 569-577 (Lecture Notes in Electrical Engineering; Vol. 733 LNEE).

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

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AU - Rai, Shwetha

AU - M, Geetha

AU - Kumar, Preetham

PY - 2021

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N2 - Itemset mining discovers interesting patterns in the dataset. The itemset may be frequent or it can be rare based on its occurrence in the database. It has been observed that most of the algorithms are designed for mining frequent itemsets. However, discovery of rare itemsets is equally important since they play a major role in making decisions in some situations. The efficiency of the algorithms depend on the way in which the data structures are designed to store and retrieve the data. Hyperlinked Rare Pattern Mining algorithm discovers all rare itemsets and is suitable for sparse dataset. In this algorithm item_id and its support count are stored in Support and Header tables. This redundancy is removed in the proposed algorithm to improve the time efficiency. An experimental analysis is conducted to discover the rare itemsets. It is observed that while there is an improvement in time efficiency, there is a tradeoff for space efficiency.

AB - Itemset mining discovers interesting patterns in the dataset. The itemset may be frequent or it can be rare based on its occurrence in the database. It has been observed that most of the algorithms are designed for mining frequent itemsets. However, discovery of rare itemsets is equally important since they play a major role in making decisions in some situations. The efficiency of the algorithms depend on the way in which the data structures are designed to store and retrieve the data. Hyperlinked Rare Pattern Mining algorithm discovers all rare itemsets and is suitable for sparse dataset. In this algorithm item_id and its support count are stored in Support and Header tables. This redundancy is removed in the proposed algorithm to improve the time efficiency. An experimental analysis is conducted to discover the rare itemsets. It is observed that while there is an improvement in time efficiency, there is a tradeoff for space efficiency.

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Rai S , M G , Kumar P. Efficient Mining of Rare Itemsets. In Bindhu V, Tavares JM, Boulogeorgos AAA, Vuppalapati C, editors, International Conference on Communication, Computing and Electronics Systems - Proceedings of ICCCES 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 569-577. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-33-4909-4_43

Efficient Mining of Rare Itemsets

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