Reframing in Frequent Pattern Mining

Chowdhury Farhan Ahmed; Md. Samiullah; Nicolas Lachiche; Meelis Kull; Peter A Flach

doi:10.1109/ICTAI.2015.118

Reframing in Frequent Pattern Mining

Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A Flach

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

4 Citations (Scopus)

285 Downloads (Pure)

Abstract

Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.

Original language	English
Title of host publication	2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015)
Subtitle of host publication	Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	799-806
Number of pages	8
ISBN (Electronic)	9781509001637
ISBN (Print)	9781509001644
DOIs	https://doi.org/10.1109/ICTAI.2015.118
Publication status	Published - Apr 2016

Publication series

Name	Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)	1082-3409

Structured keywords

Jean Golding

Keywords

Data Mining
Frequent Pattern Mining
Dataset Shift
Machine Learning
Adaptation

Access to Document

10.1109/ICTAI.2015.118

Full-text PDF (accepted author manuscript)
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Accepted author manuscript, 1.7 MB

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2 Finished

SPHERE (EPSRC IRC)
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
REFrAMe
Flach, P. A.
Engineering and Physical Sciences Research Council
1/02/13 → 1/08/16
Project: Research

Cite this

Ahmed, C. F., Samiullah, M., Lachiche, N., Kull, M., & Flach, P. A. (2016). Reframing in Frequent Pattern Mining. In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015): Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy (pp. 799-806). (Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICTAI.2015.118

Ahmed, Chowdhury Farhan ; Samiullah, Md. ; Lachiche, Nicolas ; Kull, Meelis ; Flach, Peter A. / Reframing in Frequent Pattern Mining. 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015): Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 799-806 (Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)).

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title = "Reframing in Frequent Pattern Mining",

abstract = "Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.",

keywords = "Data Mining, Frequent Pattern Mining, Dataset Shift, Machine Learning, Adaptation",

author = "Ahmed, {Chowdhury Farhan} and Md. Samiullah and Nicolas Lachiche and Meelis Kull and Flach, {Peter A}",

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series = "Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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booktitle = "2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015)",

address = "United States",

}

Ahmed, CF, Samiullah, M, Lachiche, N, Kull, M & Flach, PA 2016, Reframing in Frequent Pattern Mining. in 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015): Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy. Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Institute of Electrical and Electronics Engineers (IEEE), pp. 799-806. https://doi.org/10.1109/ICTAI.2015.118

Reframing in Frequent Pattern Mining. / Ahmed, Chowdhury Farhan; Samiullah, Md.; Lachiche, Nicolas; Kull, Meelis; Flach, Peter A.

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015): Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 799-806 (Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Reframing in Frequent Pattern Mining

AU - Ahmed, Chowdhury Farhan

AU - Samiullah, Md.

AU - Lachiche, Nicolas

AU - Kull, Meelis

AU - Flach, Peter A

PY - 2016/4

Y1 - 2016/4

N2 - Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.

AB - Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.

KW - Data Mining

KW - Frequent Pattern Mining

KW - Dataset Shift

KW - Machine Learning

KW - Adaptation

U2 - 10.1109/ICTAI.2015.118

DO - 10.1109/ICTAI.2015.118

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509001644

T3 - Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

SP - 799

EP - 806

BT - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Ahmed CF, Samiullah M, Lachiche N, Kull M, Flach PA. Reframing in Frequent Pattern Mining. In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015): Proceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 799-806. (Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)). https://doi.org/10.1109/ICTAI.2015.118

Reframing in Frequent Pattern Mining

Abstract

Publication series

Structured keywords

Keywords

Access to Document

SPHERE (EPSRC IRC)

REFrAMe

Cite this