Article

Free access

Data mining, hypergraph transversals, and machine learning (extended abstract)

Authors:

Dimitrios Gunopulos,

Heikki Mannila,

Roni Khardon,

Hannu ToivonenAuthors Info & Claims

PODS '97: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

Pages 209 - 216

https://doi.org/10.1145/263661.263684

Published: 01 May 1997 Publication History

PDF eReader

References

[1]

R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD Conference on Management of Data (SIGMOD'93), pages 207 - 216, May 1993.

Digital Library

Google Scholar

[2]

It. Agrawal, H. Mannila, It. Sdka~t, It. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapixo, P. Smyth, and It. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307 - 328. AAAI Press, Menlo Park, CA, 1996.

Digital Library

Google Scholar

[3]

D. Angluia. Queries and concept learning. Machine Learning, 2(4):319 - 342, Apr. 1988.

Digital Library

Google Scholar

[4]

C. Berge. Hypergraphs. Combinatorics of Finite Sets. North-Holland Publishing Company, Amsterdam, 3rd edition, I973.

Google Scholar

[5]

N. H. Bshouty, P~. Cleve, R. Gavalda, S. Kannan, and C. Tamon. Oracles and queries that are sufficient for exact learning. Journal of Computer and System Sciences, 52:421 - 433, 1996.

Digital Library

Google Scholar

[6]

L. De Itaedt and M. Bruynooghe. A theory of clausal discovery. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCA!-93), pages 1058- 1053, Chamb~ry, France, 1993. Morgan Kaufraann.

Google Scholar

[7]

L. De Raedt and S. D~erosld. First-order fl~-clausal theories are PAG-learnable. Artificial Intelligence, 70:375 - 392, 1994.

Digital Library

Google Scholar

[8]

T. Eiter and G. Gotflob. Identifying the minimal transversals of a hypergraph and related problems. SIAM Journal on Computing, 24(6):1278 - 1304, Dec. 1995.

Digital Library

Google Scholar

[9]

U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data minting to knowledge discovery: An overview. In U. M. Fayyad, G. Piatetsky-Shapixo, P. Smyth, and It. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 1- 34. AAAI Press, Menlo Park, CA, 1996.

Digital Library

Google Scholar

[10]

M. Fredman and L. Khachiyan. On the complexity of dualization of monotone disjunctive normal forms. Journal of Algorithms, 21:618 - 628, 1996.

Digital Library

Google Scholar

[11]

D. Gunopulos, H. Mannila, and S. Saluja. Discovering all most specific sentences by randomized algorithms. In Proceedings of the International Conference on Database Theory (IGDT'97), pages 215 - 229, Delphi, Greece, Jan. 1997.

Digital Library

Google Scholar

[12]

It. Khaxdon. Translating between Horn representations and their characteristic models. Journal of AI Research, 3:349- 372, 1995.

Digital Library

Google Scholar

[13]

J.-U. Kietz and S. Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive Logic Programming, pages 335 - 359. Academic Press, London, 1992.

Google Scholar

[14]

W. Kloesgen. Efficient discovery of interesting statements in databases. Journal of Intelligent Information Systems, 4(1):53 - 69, 1995.

Crossref

Google Scholar

[15]

P. Langley. Elements of Machine Learning. Morgazl Kanfmann, San biateo, CA, 1995.

Digital Library

Google Scholar

[16]

It. Mannila and K.-J. R~211~. Design by example: An application of Armstrong relations. Journal of Computer and System Sciences, 33(2):126 - 141, 1986.

Digital Library

Google Scholar

[17]

H. Mannila and K.-J. R~ih~. Design of Relational Databases. Addison-Wesley Publishing Company, Wokingham, UK, 1992.

Digital Library

Google Scholar

[18]

It. Mannila and K.-J. R~hL Algorithms for inferring functional dependencies. Data ~ Knowledge Engineering, 12(1):83- 99, Feb. 1994.

Digital Library

Google Scholar

[19]

H. Mannila and H. Toivonen. On an algorithm for finding all interesting sentences. In Cybernetics and Systems, Volume II, The Thirteenth European Meeting on Cybernetics and Systems Research, pages 973 - 978, Vienna, Austria, Apr. 1996. Extended version available as: Levehvise search and borders of theories in knowledge discovery, Report C-1997-8, University of Helsinld, Depaxtment of Computer Science.

Google Scholar

[20]

H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules. In Knowledge Discovery in Databases, Papers from the 1994 AAAI Workshop (KDD'94), pages 181 - 192, Seattle, Washington, July 1994.

Google Scholar

[21]

H. Mannila, H. Toivonen, and A. I. Verkanao. Discovering frequent episodes in sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD'95), pages 210-- 215, Montreal, Canada, Aug. 1995.

Google Scholar

[22]

N. Misra and L. Pitt. On bounded-degree hypergraph transversals. Manuscript., 1995.

Google Scholar

[23]

T. M. Mitchell. Generalization as search. Artificial intelligence, 18:203 - 226, 1982.

Google Scholar

[24]

C. Tamon. Private communication. 1997.

Google Scholar

Cited By

View all

Chen HConte AGrossi RLoukides GPissis SSweering M(2024)On Breaking Truss-Based and Core-Based CommunitiesACM Transactions on Knowledge Discovery from Data10.1145/3644077Online publication date: 14-Feb-2024
https://doi.org/10.1145/3644077
Babu AJohn S(2024)Persistent homology based Bottleneck distance in hypergraph productsApplied Network Science10.1007/s41109-024-00617-39:1Online publication date: 23-Apr-2024
https://doi.org/10.1007/s41109-024-00617-3
Bartier VDefrain OMc Inerney F(2024)Hypergraph Dualization with $$\textsf{FPT}$$-delay Parameterized by the Degeneracy and DimensionCombinatorial Algorithms10.1007/978-3-031-63021-7_9(111-125)Online publication date: 22-Jun-2024
https://doi.org/10.1007/978-3-031-63021-7_9
Show More Cited By

Index Terms

Data mining, hypergraph transversals, and machine learning (extended abstract)

Recommendations

Cycle transversals in perfect graphs and cographs

A cycle transversal (or feedback vertex set) of a graph G is a subset T@__ __V(G) such that T@__ __V(C)<>0@__ __ for every cycle C of G. This work considers the problem of finding special cycle transversals in perfect graphs and cographs. We prove that ...
Maximum Clique Transversals
WG '01: Proceedings of the 27th International Workshop on Graph-Theoretic Concepts in Computer Science

A maximum clique transversal set in a graph G is a set S of vertices such that every maximum clique of G contains at least a vertex in S . Clearly, removing a maximum clique transversal set reduces the clique number of a graph. We study ...
Transversals and domination in uniform hypergraphs

Let H=(V,E) be a hypergraph with vertex set V and edge set E of order n"H=|V| and size m"H=|E|. A transversal in H is a subset of vertices in H that has a nonempty intersection with every edge of H. The transversal number @t(H) of H is the minimum size ...

Comments

Information & Contributors

Information

Published In

PODS '97: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

May 1997

272 pages

ISBN:0897919106

DOI:10.1145/263661

Chairmen:
Alberto Mendelzon
Univ. of Toronto, Toronto, Ont., Canada
,
Z. Meral Özsoyoglu
Case Western Reserve Univ., Cleveland, OH

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1997

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS97

Sponsor:

SIGMOD

SIGMOD/PODS97: Joint ACM SIGMOD International Conference on Management of Data and ACM Symposium on Principles of Database Systems

May 11 - 15, 1997

Arizona, Tucson, USA

Acceptance Rates

PODS '97 Paper Acceptance Rate 23 of 118 submissions, 19%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

130
Total Citations
View Citations
1,386
Total Downloads

Downloads (Last 12 months)113
Downloads (Last 6 weeks)17

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chen HConte AGrossi RLoukides GPissis SSweering M(2024)On Breaking Truss-Based and Core-Based CommunitiesACM Transactions on Knowledge Discovery from Data10.1145/3644077Online publication date: 14-Feb-2024
https://doi.org/10.1145/3644077
Babu AJohn S(2024)Persistent homology based Bottleneck distance in hypergraph productsApplied Network Science10.1007/s41109-024-00617-39:1Online publication date: 23-Apr-2024
https://doi.org/10.1007/s41109-024-00617-3
Bartier VDefrain OMc Inerney F(2024)Hypergraph Dualization with $$\textsf{FPT}$$-delay Parameterized by the Degeneracy and DimensionCombinatorial Algorithms10.1007/978-3-031-63021-7_9(111-125)Online publication date: 22-Jun-2024
https://doi.org/10.1007/978-3-031-63021-7_9
Manco GRitacco ERullo ASaccà DSerra E(2022)Machine learning methods for generating high dimensional discrete datasetsWIREs Data Mining and Knowledge Discovery10.1002/widm.145012:2Online publication date: 18-Jan-2022
https://doi.org/10.1002/widm.1450
Martin TFrancoeur GValtchev PGupta RLiu YShah MRajan STang JPrakash B(2020)CICLAD: A Fast and Memory-efficient Closed Itemset Miner for StreamsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403232(1810-1818)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403232
Alba-Cabrera EGodoy-Calderon SLazo-Cortes MFco. Martinez-Trinidad JCarrasco-Ochoa J(2019)On the Relation Between the Concepts of Irreducible Testor and Minimal TransversalIEEE Access10.1109/ACCESS.2019.29222317(82809-82816)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2922231
Elbassioni KRauf IRay S(2019)A global parallel algorithm for enumerating minimal transversals of geometric hypergraphsTheoretical Computer Science10.1016/j.tcs.2018.09.027767:C(26-33)Online publication date: 3-May-2019
https://dl.acm.org/doi/10.1016/j.tcs.2018.09.027
Van Lierde HChow T(2019)Query-oriented text summarization based on hypergraph transversalsInformation Processing and Management: an International Journal10.1016/j.ipm.2019.03.00356:4(1317-1338)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.ipm.2019.03.003
Saccà DSerra E(2019)Number of Minimal Hypergraph Transversals and Complexity of IFM with Infrequency: High in Theory, but Often Not so Much in Practice!AI*IA 2019 – Advances in Artificial Intelligence10.1007/978-3-030-35166-3_14(193-209)Online publication date: 12-Nov-2019
https://doi.org/10.1007/978-3-030-35166-3_14
Defrain ONourine L(2019)Dualization in Lattices Given by Implicational BasesFormal Concept Analysis10.1007/978-3-030-21462-3_7(89-98)Online publication date: 23-May-2019
https://doi.org/10.1007/978-3-030-21462-3_7
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Cycle transversals in perfect graphs and cographs

Maximum Clique Transversals

Transversals and domination in uniform hypergraphs

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

PDF

eReader

Login options

Full Access

References

Cited By

Index Terms

Recommendations

Cycle transversals in perfect graphs and cographs

Maximum Clique Transversals

Transversals and domination in uniform hypergraphs

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations