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Closed frequent itemset mining with arbitrary side constraints

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Closed frequent itemset mining with arbitrary side constraints. / Kocak, Gokberk; Akgun, Ozgur; Miguel, Ian James; Nightingale, Peter William.

2018 IEEE International Conference on Data Mining Workshops (ICDMW). ed. / Hanghang Tong; Zhenhui (Jessie) Li; Feida Zhu; Jeffrey Yu. IEEE Computer Society, 2018. p. 1224 - 1232 8637581.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Kocak, G, Akgun, O, Miguel, IJ & Nightingale, PW 2018, Closed frequent itemset mining with arbitrary side constraints. in H Tong, ZJ Li, F Zhu & J Yu (eds), 2018 IEEE International Conference on Data Mining Workshops (ICDMW)., 8637581, IEEE Computer Society, pp. 1224 - 1232, Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM 2018), Sentosa Island, Singapore, 17/11/18. https://doi.org/10.1109/ICDMW.2018.00175

APA

Kocak, G., Akgun, O., Miguel, I. J., & Nightingale, P. W. (2018). Closed frequent itemset mining with arbitrary side constraints. In H. Tong, Z. J. Li, F. Zhu, & J. Yu (Eds.), 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1224 - 1232). [8637581] IEEE Computer Society. https://doi.org/10.1109/ICDMW.2018.00175

Vancouver

Kocak G, Akgun O, Miguel IJ, Nightingale PW. Closed frequent itemset mining with arbitrary side constraints. In Tong H, Li ZJ, Zhu F, Yu J, editors, 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE Computer Society. 2018. p. 1224 - 1232. 8637581 https://doi.org/10.1109/ICDMW.2018.00175

Author

Kocak, Gokberk ; Akgun, Ozgur ; Miguel, Ian James ; Nightingale, Peter William. / Closed frequent itemset mining with arbitrary side constraints. 2018 IEEE International Conference on Data Mining Workshops (ICDMW). editor / Hanghang Tong ; Zhenhui (Jessie) Li ; Feida Zhu ; Jeffrey Yu. IEEE Computer Society, 2018. pp. 1224 - 1232

Bibtex - Download

@inproceedings{921a03b374654acdb3cf8b608e1ef86a,
title = "Closed frequent itemset mining with arbitrary side constraints",
abstract = "Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data. For large datasets the number of frequent itemsets can also be very large, defeating their purpose. Therefore, several extensions to FIM have been studied, such as adding high-utility (or low-cost) constraints and only finding closed (or maximal) frequent itemsets. This paper presents a constraint programming based approach that combines arbitrary side constraints with closed frequent itemset mining. Our approach allows arbitrary side constraints to be expressed in a high level and declarative language which is then translated automatically for efficient solution by a SAT solver. We compare our approach with state-of-the-art algorithms via the MiningZinc system (where possible) and show significant contributions in terms of performance and applicability.",
keywords = "Data mining, Pattern mining, Frequent itemset mining, Closed frequent itemset mining, Constraint modelling",
author = "Gokberk Kocak and Ozgur Akgun and Miguel, {Ian James} and Nightingale, {Peter William}",
year = "2018",
month = "11",
day = "17",
doi = "10.1109/ICDMW.2018.00175",
language = "English",
isbn = "9781538692899",
pages = "1224 -- 1232",
editor = "Hanghang Tong and Li, {Zhenhui (Jessie)} and Feida Zhu and Jeffrey Yu",
booktitle = "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
publisher = "IEEE Computer Society",
address = "United States",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Closed frequent itemset mining with arbitrary side constraints

AU - Kocak, Gokberk

AU - Akgun, Ozgur

AU - Miguel, Ian James

AU - Nightingale, Peter William

PY - 2018/11/17

Y1 - 2018/11/17

N2 - Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data. For large datasets the number of frequent itemsets can also be very large, defeating their purpose. Therefore, several extensions to FIM have been studied, such as adding high-utility (or low-cost) constraints and only finding closed (or maximal) frequent itemsets. This paper presents a constraint programming based approach that combines arbitrary side constraints with closed frequent itemset mining. Our approach allows arbitrary side constraints to be expressed in a high level and declarative language which is then translated automatically for efficient solution by a SAT solver. We compare our approach with state-of-the-art algorithms via the MiningZinc system (where possible) and show significant contributions in terms of performance and applicability.

AB - Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data. For large datasets the number of frequent itemsets can also be very large, defeating their purpose. Therefore, several extensions to FIM have been studied, such as adding high-utility (or low-cost) constraints and only finding closed (or maximal) frequent itemsets. This paper presents a constraint programming based approach that combines arbitrary side constraints with closed frequent itemset mining. Our approach allows arbitrary side constraints to be expressed in a high level and declarative language which is then translated automatically for efficient solution by a SAT solver. We compare our approach with state-of-the-art algorithms via the MiningZinc system (where possible) and show significant contributions in terms of performance and applicability.

KW - Data mining

KW - Pattern mining

KW - Frequent itemset mining

KW - Closed frequent itemset mining

KW - Constraint modelling

U2 - 10.1109/ICDMW.2018.00175

DO - 10.1109/ICDMW.2018.00175

M3 - Conference contribution

SN - 9781538692899

SP - 1224

EP - 1232

BT - 2018 IEEE International Conference on Data Mining Workshops (ICDMW)

A2 - Tong, Hanghang

A2 - Li, Zhenhui (Jessie)

A2 - Zhu, Feida

A2 - Yu, Jeffrey

PB - IEEE Computer Society

ER -

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ID: 256767847