Constrained Independence for Detecting Interesting Patterns

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Constrained Independence for Detecting Interesting Patterns. / Delacroix, Thomas; Boubekki, Ahcène; Lenca, Philippe et al.
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Hrsg. / Gabriella Pasi; James Kwok; Osmar Zaiane; Patrick Gallinari; Eric Gaussier; Longbing Cao. IEEE - Institute of Electrical and Electronics Engineers Inc., 2015. 7344897 (Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Delacroix, T, Boubekki, A, Lenca, P & Lallich, S 2015, Constrained Independence for Detecting Interesting Patterns. in G Pasi, J Kwok, O Zaiane, P Gallinari, E Gaussier & L Cao (Hrsg.), 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)., 7344897, Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, IEEE - Institute of Electrical and Electronics Engineers Inc., IEEE International Conference on Data Science and Advanced Analytics - DSAA 2015, Paris, Frankreich, 19.10.15. https://doi.org/10.1109/DSAA.2015.7344897

APA

Delacroix, T., Boubekki, A., Lenca, P., & Lallich, S. (2015). Constrained Independence for Detecting Interesting Patterns. In G. Pasi, J. Kwok, O. Zaiane, P. Gallinari, E. Gaussier, & L. Cao (Hrsg.), 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Artikel 7344897 (Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2015.7344897

Vancouver

Delacroix T, Boubekki A, Lenca P, Lallich S. Constrained Independence for Detecting Interesting Patterns. in Pasi G, Kwok J, Zaiane O, Gallinari P, Gaussier E, Cao L, Hrsg., 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE - Institute of Electrical and Electronics Engineers Inc. 2015. 7344897. (Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015). doi: 10.1109/DSAA.2015.7344897

Bibtex

@inbook{53d0848465fe4b19aaa128d35b27c5e7,
title = "Constrained Independence for Detecting Interesting Patterns",
abstract = "Among other criteria, a pattern may be interesting if it is not redundant with other discovered patterns. A general approach to determining redundancy is to consider a probabilistic model for frequencies of patterns, based on those of patterns already mined, and compare observed frequencies to the model. Such probabilistic models include the independence model, partition models or more complex models which are approached via randomization for a lack of an adequate tool in probability theory allowing a direct approach. We define constrained independence, a generalization to the notion of independence. This tool allows us to describe probabilistic models for evaluating redundancy in frequent itemset mining. We provide algorithms, integrated within the mining process, for determining non-redundant itemsets. Through experimentations, we show that the models used reveal high rates of redundancy among frequent itemsets and we extract the most interesting ones.",
keywords = "Informatics, Mathematics, Business informatics",
author = "Thomas Delacroix and Ahc{\`e}ne Boubekki and Philippe Lenca and St{\'e}phane Lallich",
year = "2015",
month = dec,
day = "2",
doi = "10.1109/DSAA.2015.7344897",
language = "English",
series = "Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
editor = "Gabriella Pasi and James Kwok and Osmar Zaiane and Patrick Gallinari and Eric Gaussier and Longbing Cao",
booktitle = "2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)",
address = "United States",
note = "IEEE International Conference on Data Science and Advanced Analytics - DSAA 2015, DSAA Conference 2015 ; Conference date: 19-10-2015 Through 21-10-2015",
url = "http://dsaa2015.lip6.fr/",

}

RIS

TY - CHAP

T1 - Constrained Independence for Detecting Interesting Patterns

AU - Delacroix, Thomas

AU - Boubekki, Ahcène

AU - Lenca, Philippe

AU - Lallich, Stéphane

PY - 2015/12/2

Y1 - 2015/12/2

N2 - Among other criteria, a pattern may be interesting if it is not redundant with other discovered patterns. A general approach to determining redundancy is to consider a probabilistic model for frequencies of patterns, based on those of patterns already mined, and compare observed frequencies to the model. Such probabilistic models include the independence model, partition models or more complex models which are approached via randomization for a lack of an adequate tool in probability theory allowing a direct approach. We define constrained independence, a generalization to the notion of independence. This tool allows us to describe probabilistic models for evaluating redundancy in frequent itemset mining. We provide algorithms, integrated within the mining process, for determining non-redundant itemsets. Through experimentations, we show that the models used reveal high rates of redundancy among frequent itemsets and we extract the most interesting ones.

AB - Among other criteria, a pattern may be interesting if it is not redundant with other discovered patterns. A general approach to determining redundancy is to consider a probabilistic model for frequencies of patterns, based on those of patterns already mined, and compare observed frequencies to the model. Such probabilistic models include the independence model, partition models or more complex models which are approached via randomization for a lack of an adequate tool in probability theory allowing a direct approach. We define constrained independence, a generalization to the notion of independence. This tool allows us to describe probabilistic models for evaluating redundancy in frequent itemset mining. We provide algorithms, integrated within the mining process, for determining non-redundant itemsets. Through experimentations, we show that the models used reveal high rates of redundancy among frequent itemsets and we extract the most interesting ones.

KW - Informatics

KW - Mathematics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84962853098&partnerID=8YFLogxK

U2 - 10.1109/DSAA.2015.7344897

DO - 10.1109/DSAA.2015.7344897

M3 - Article in conference proceedings

T3 - Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015

BT - 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

A2 - Pasi, Gabriella

A2 - Kwok, James

A2 - Zaiane, Osmar

A2 - Gallinari, Patrick

A2 - Gaussier, Eric

A2 - Cao, Longbing

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Data Science and Advanced Analytics - DSAA 2015

Y2 - 19 October 2015 through 21 October 2015

ER -

DOI