Efficient discovery of sets of co-occurring items in event sequences

Boris Cule; Len Feremans; Bart Goethals

doi:10.1007/978-3-319-46128-1_23

Efficient discovery of sets of co-occurring items in event sequences

Boris Cule, Len Feremans, Bart Goethals

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

7 Citations (Scopus)

Abstract

Discovering patterns in long event sequences is an important data mining task. Most existing work focuses on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion—a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing a novel pruning technique in order to reduce the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. The data and software related to this paper are available at https://bitbucket.org/len feremans/ sequencepatternmining public.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I
Editors	Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
Publisher	Springer
Pages	361-377
Number of pages	17
ISBN (Electronic)	9783319461281
ISBN (Print)	9783319461274
DOIs	https://doi.org/10.1007/978-3-319-46128-1_23
Publication status	Published - 2016
Event	European Conference on Machine Learning and Knowledge Discovery in Databases 2016 - Riva del Garda, Italy Duration: 19 Sept 2016 → 23 Sept 2016 Conference number: 15th http://www.ecmlpkdd2016.org/ https://link.springer.com/book/10.1007/978-3-319-46128-1 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9851 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Knowledge Discovery in Databases 2016
Abbreviated title	ECML PKDD 2016
Country/Territory	Italy
City	Riva del Garda
Period	19/09/16 → 23/09/16
Internet address	http://www.ecmlpkdd2016.org/ https://link.springer.com/book/10.1007/978-3-319-46128-1 (Proceedings)

Access to Document

10.1007/978-3-319-46128-1_23

Cite this

Cule, B., Feremans, L., & Goethals, B. (2016). Efficient discovery of sets of co-occurring items in event sequences. In P. Frasconi, N. Landwehr, G. Manco, & J. Vreeken (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I (pp. 361-377). (Lecture Notes in Computer Science ; Vol. 9851 LNAI). Springer. https://doi.org/10.1007/978-3-319-46128-1_23

Cule, Boris ; Feremans, Len ; Goethals, Bart. / Efficient discovery of sets of co-occurring items in event sequences. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I. editor / Paolo Frasconi ; Niels Landwehr ; Giuseppe Manco ; Jilles Vreeken. Springer, 2016. pp. 361-377 (Lecture Notes in Computer Science ).

@inproceedings{23127ebc59e243d0b59b630b31b0bb85,

title = "Efficient discovery of sets of co-occurring items in event sequences",

abstract = "Discovering patterns in long event sequences is an important data mining task. Most existing work focuses on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion—a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing a novel pruning technique in order to reduce the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. The data and software related to this paper are available at https://bitbucket.org/len feremans/ sequencepatternmining public.",

author = "Boris Cule and Len Feremans and Bart Goethals",

year = "2016",

doi = "10.1007/978-3-319-46128-1_23",

language = "English",

isbn = "9783319461274",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

pages = "361--377",

editor = "Frasconi, {Paolo } and Landwehr, {Niels } and Manco, {Giuseppe } and Vreeken, {Jilles }",

booktitle = "Machine Learning and Knowledge Discovery in Databases",

note = "European Conference on Machine Learning and Knowledge Discovery in Databases 2016, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",

url = "http://www.ecmlpkdd2016.org/, https://link.springer.com/book/10.1007/978-3-319-46128-1",

}

Cule, B, Feremans, L & Goethals, B 2016, Efficient discovery of sets of co-occurring items in event sequences. in P Frasconi, N Landwehr, G Manco & J Vreeken (eds), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I. Lecture Notes in Computer Science , vol. 9851 LNAI, Springer, pp. 361-377, European Conference on Machine Learning and Knowledge Discovery in Databases 2016, Riva del Garda, Italy, 19/09/16. https://doi.org/10.1007/978-3-319-46128-1_23

Efficient discovery of sets of co-occurring items in event sequences. / Cule, Boris; Feremans, Len; Goethals, Bart.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I. ed. / Paolo Frasconi; Niels Landwehr; Giuseppe Manco; Jilles Vreeken. Springer, 2016. p. 361-377 (Lecture Notes in Computer Science ; Vol. 9851 LNAI).

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

TY - GEN

T1 - Efficient discovery of sets of co-occurring items in event sequences

AU - Cule, Boris

AU - Feremans, Len

AU - Goethals, Bart

N1 - Conference code: 15th

PY - 2016

Y1 - 2016

N2 - Discovering patterns in long event sequences is an important data mining task. Most existing work focuses on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion—a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing a novel pruning technique in order to reduce the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. The data and software related to this paper are available at https://bitbucket.org/len feremans/ sequencepatternmining public.

AB - Discovering patterns in long event sequences is an important data mining task. Most existing work focuses on frequency-based quality measures that allow algorithms to use the anti-monotonicity property to prune the search space and efficiently discover the most frequent patterns. In this work, we step away from such measures, and evaluate patterns using cohesion—a measure of how close to each other the items making up the pattern appear in the sequence on average. We tackle the fact that cohesion is not an anti-monotonic measure by developing a novel pruning technique in order to reduce the search space. By doing so, we are able to efficiently unearth rare, but strongly cohesive, patterns that existing methods often fail to discover. The data and software related to this paper are available at https://bitbucket.org/len feremans/ sequencepatternmining public.

UR - http://www.scopus.com/inward/record.url?scp=84988589084&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46128-1_23

DO - 10.1007/978-3-319-46128-1_23

M3 - Conference Paper

AN - SCOPUS:84988589084

SN - 9783319461274

T3 - Lecture Notes in Computer Science

SP - 361

EP - 377

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Frasconi, Paolo

A2 - Landwehr, Niels

A2 - Manco, Giuseppe

A2 - Vreeken, Jilles

PB - Springer

T2 - European Conference on Machine Learning and Knowledge Discovery in Databases 2016

Y2 - 19 September 2016 through 23 September 2016

ER -

Cule B, Feremans L, Goethals B. Efficient discovery of sets of co-occurring items in event sequences. In Frasconi P, Landwehr N, Manco G, Vreeken J, editors, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19–23, 2016, Proceedings, Part I. Springer. 2016. p. 361-377. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-319-46128-1_23

Efficient discovery of sets of co-occurring items in event sequences

Abstract

Publication series

Conference

Access to Document

Other files and links

Cite this