An Efficient Approach for Mining Frequent Subgraphs

Tahira Alam; Sabit Anwar Zahin; Md Samiullah; Chowdhury Farhan Ahmed

doi:10.1007/978-3-319-69900-4_62

An Efficient Approach for Mining Frequent Subgraphs

Tahira Alam
, Sabit Anwar Zahin
, Md Samiullah
, Chowdhury Farhan Ahmed

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

2 Citations (Scopus)

Abstract

Graph-based data mining techniques, known as graph mining, are capable of modeling several real-life complex structures such as roads, maps, computer or social networks and chemical structures by graphs. Useful information can be mined by discovering the frequent subgraphs. However, the existing approaches to mine frequent subgraphs have significant drawbacks in terms of efficiency. In this paper, we focus on real-time frequent subgraph mining and propose an efficient customized data structure and technique to reduce subgraph isomorphism checking as well as a supergraph based optimized descendant generation algorithm. Extensive performance analyses prove the efficiency of our algorithm over the existing methods.

Original language	English
Title of host publication	Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings
Editors	B. Uma Shankar, Kuntal Ghosh, Deba Prasad Mandal, Shubhra Sankar Ray, Sankar K. Pal, David Zhang
Publisher	Springer
Pages	486-492
Number of pages	7
ISBN (Print)	9783319698991
DOIs	https://doi.org/10.1007/978-3-319-69900-4_62
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Pattern Recognition and Machine Intelligence 2017 - Kolkata, India Duration: 5 Dec 2017 → 8 Dec 2017 Conference number: 7th https://link.springer.com/book/10.1007/978-3-319-69900-4 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Volume	10597
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Pattern Recognition and Machine Intelligence 2017
Abbreviated title	PReMI 2017
Country/Territory	India
City	Kolkata
Period	5/12/17 → 8/12/17
Internet address	https://link.springer.com/book/10.1007/978-3-319-69900-4 (Proceedings)

Keywords

Data mining
Frequent patterns
Graph mining
Knowledge discovery

Access to Document

10.1007/978-3-319-69900-4_62

Cite this

Alam, T., Zahin, S. A., Samiullah, M., & Ahmed, C. F. (2017). An Efficient Approach for Mining Frequent Subgraphs. In B. U. Shankar, K. Ghosh, D. P. Mandal, S. S. Ray, S. K. Pal, & D. Zhang (Eds.), Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings (pp. 486-492). (Lecture Notes in Computer Science; Vol. 10597). Springer. https://doi.org/10.1007/978-3-319-69900-4_62

Alam, Tahira ; Zahin, Sabit Anwar ; Samiullah, Md et al. / An Efficient Approach for Mining Frequent Subgraphs. Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings. editor / B. Uma Shankar ; Kuntal Ghosh ; Deba Prasad Mandal ; Shubhra Sankar Ray ; Sankar K. Pal ; David Zhang. Springer, 2017. pp. 486-492 (Lecture Notes in Computer Science).

@inproceedings{839619731f1b4db288cd66ff9c0972a4,

title = "An Efficient Approach for Mining Frequent Subgraphs",

abstract = "Graph-based data mining techniques, known as graph mining, are capable of modeling several real-life complex structures such as roads, maps, computer or social networks and chemical structures by graphs. Useful information can be mined by discovering the frequent subgraphs. However, the existing approaches to mine frequent subgraphs have significant drawbacks in terms of efficiency. In this paper, we focus on real-time frequent subgraph mining and propose an efficient customized data structure and technique to reduce subgraph isomorphism checking as well as a supergraph based optimized descendant generation algorithm. Extensive performance analyses prove the efficiency of our algorithm over the existing methods.",

keywords = "Data mining, Frequent patterns, Graph mining, Knowledge discovery",

author = "Tahira Alam and Zahin, \{Sabit Anwar\} and Md Samiullah and Ahmed, \{Chowdhury Farhan\}",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; International Conference on Pattern Recognition and Machine Intelligence 2017, PReMI 2017 ; Conference date: 05-12-2017 Through 08-12-2017",

year = "2017",

doi = "10.1007/978-3-319-69900-4\_62",

language = "English",

isbn = "9783319698991",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "486--492",

editor = "Shankar, \{B. Uma\} and Kuntal Ghosh and Mandal, \{Deba Prasad\} and Ray, \{Shubhra Sankar\} and Pal, \{Sankar K.\} and David Zhang",

booktitle = "Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings",

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url = "https://link.springer.com/book/10.1007/978-3-319-69900-4",

}

Alam, T, Zahin, SA, Samiullah, M & Ahmed, CF 2017, An Efficient Approach for Mining Frequent Subgraphs. in BU Shankar, K Ghosh, DP Mandal, SS Ray, SK Pal & D Zhang (eds), Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings. Lecture Notes in Computer Science, vol. 10597, Springer, pp. 486-492, International Conference on Pattern Recognition and Machine Intelligence 2017, Kolkata, India, 5/12/17. https://doi.org/10.1007/978-3-319-69900-4_62

An Efficient Approach for Mining Frequent Subgraphs. / Alam, Tahira; Zahin, Sabit Anwar; Samiullah, Md et al.
Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings. ed. / B. Uma Shankar; Kuntal Ghosh; Deba Prasad Mandal; Shubhra Sankar Ray; Sankar K. Pal; David Zhang. Springer, 2017. p. 486-492 (Lecture Notes in Computer Science; Vol. 10597).

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

TY - GEN

T1 - An Efficient Approach for Mining Frequent Subgraphs

AU - Alam, Tahira

AU - Zahin, Sabit Anwar

AU - Samiullah, Md

AU - Ahmed, Chowdhury Farhan

N1 - Conference code: 7th

PY - 2017

Y1 - 2017

N2 - Graph-based data mining techniques, known as graph mining, are capable of modeling several real-life complex structures such as roads, maps, computer or social networks and chemical structures by graphs. Useful information can be mined by discovering the frequent subgraphs. However, the existing approaches to mine frequent subgraphs have significant drawbacks in terms of efficiency. In this paper, we focus on real-time frequent subgraph mining and propose an efficient customized data structure and technique to reduce subgraph isomorphism checking as well as a supergraph based optimized descendant generation algorithm. Extensive performance analyses prove the efficiency of our algorithm over the existing methods.

AB - Graph-based data mining techniques, known as graph mining, are capable of modeling several real-life complex structures such as roads, maps, computer or social networks and chemical structures by graphs. Useful information can be mined by discovering the frequent subgraphs. However, the existing approaches to mine frequent subgraphs have significant drawbacks in terms of efficiency. In this paper, we focus on real-time frequent subgraph mining and propose an efficient customized data structure and technique to reduce subgraph isomorphism checking as well as a supergraph based optimized descendant generation algorithm. Extensive performance analyses prove the efficiency of our algorithm over the existing methods.

KW - Data mining

KW - Frequent patterns

KW - Graph mining

KW - Knowledge discovery

UR - https://www.scopus.com/pages/publications/85036477548

U2 - 10.1007/978-3-319-69900-4_62

DO - 10.1007/978-3-319-69900-4_62

M3 - Conference Paper

AN - SCOPUS:85036477548

SN - 9783319698991

T3 - Lecture Notes in Computer Science

SP - 486

EP - 492

BT - Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Proceedings

A2 - Shankar, B. Uma

A2 - Ghosh, Kuntal

A2 - Mandal, Deba Prasad

A2 - Ray, Shubhra Sankar

A2 - Pal, Sankar K.

A2 - Zhang, David

PB - Springer

T2 - International Conference on Pattern Recognition and Machine Intelligence 2017

Y2 - 5 December 2017 through 8 December 2017

ER -

An Efficient Approach for Mining Frequent Subgraphs

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Keywords

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