Article

Algorithms for storytelling

Authors:

Deept Kumar,

Naren Ramakrishnan,

Richard F. Helm,

Malcolm PottsAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 604 - 610

https://doi.org/10.1145/1150402.1150475

Published: 20 August 2006 Publication History

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Abstract

We formulate a new data mining problem called it storytelling as a generalization of redescription mining. In traditional redescription mining, we are given a set of objects and a collection of subsets defined over these objects. The goal is to view the set system as a vocabulary and identify two expressions in this vocabulary that induce the same set of objects. Storytelling, on the other hand, aims to explicitly relate object sets that are disjoint (and hence, maximally dissimilar) by finding a chain of (approximate) redescriptions between the sets. This problem finds applications in bioinformatics, for instance, where the biologist is trying to relate a set of genes expressed in one experiment to another set, implicated in a different pathway. We outline an efficient storytelling implementation that embeds the CART wheels redescription mining algorithm in an A* search procedure, using the former to supply next move operators on search branches to the latter. This approach is practical and effective for mining large datasets and, at the same time, exploits the structure of partitions imposed by the given vocabulary. Three application case studies are presented: a study of word overlaps in large English dictionaries, exploring connections between genesets in a bioinformatics dataset, and relating publications in the PubMed index of abstracts.

References

[1]

C. C. Aggarwal, J. L. Wolf, and P. S. Yu. A New Method for Similarity Indexing of Market Basket Data. In Proc. SIGMOD'99, pages 407--418, 1999.

Digital Library

Google Scholar

[2]

L. Getoor. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, Vol. 5(1):pages 84--89, 2003.

Digital Library

Google Scholar

[3]

R. Guha, R. Kumar, D. Sivakumar, and R. Sundaram. Unweaving a Web of Documents. In Proc. KDD'05, pages 574--579, 2005.

Digital Library

Google Scholar

[4]

A. Kuchinsky, K. Graham, D. Moh, A. Adler, K. Babaria, and M. L. Creech. Biological Storytelling: a Software Tool for Biological Information Organization based upon Narrative Structure. ACM SIGGROUP Bulletin, Vol. 23(2):pages 4--5, Aug 2002.

Digital Library

Google Scholar

[5]

M. Meila. Comparing Clusterings by the Variation of Information. In Proc. COLT'03, pages 173--187, 2003.

Crossref

Google Scholar

[6]

A. W. Moore and M. S. Lee. Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. JAIR, Vol. 8:pages 67--91, 1998.

Digital Library

Google Scholar

[7]

A. Nanopoulos and Y. Manolopoulos. Efficient Similarity Search for Market Basket Data. VLDB Journal, Vol. 11(2):pages 138--152, 2002.

Digital Library

Google Scholar

[8]

J. Neville and D. Jensen. Supporting Relational Knowledge Discovery: Lessons in Architecture and Algorithm Design. In Proc. Data Mining Lessons Learned Workshop, ICML'02, 2002.

Google Scholar

[9]

L. Parida and N. Ramakrishnan. Redescription Mining: Structure Theory and Algorithms. In Proc. AAAI'05, pages 837--844, 2005.

Digital Library

Google Scholar

[10]

N. Ramakrishnan, D. Kumar, B. Mishra, M. Potts, and R. F. Helm. Turning CARTwheels: An Alternating Algorithm for Mining Redescriptions. In Proc. KDD'04, pages 266--275, 2004.

Digital Library

Google Scholar

[11]

S. Sarawagi and A. Kirpal. Efficient Set Joins on Similarity Predicates. In Proc. SIGMOD'04, pages 743--754, June 2004.

Digital Library

Google Scholar

[12]

D. A. Simovici and S. Jaroszewicz. An Axiomatization of Partition Entropy. IEEE Transactions on Information Theory, Vol. 48(7):pages 2138--2142, 2002.

Digital Library

Google Scholar

[13]

D. R. Swanson and N. R. Smalheiser. An Interactive System for Finding Complementary Literatures: A Stimulus to Scientific Discovery. Artificial Intelligence, Vol. 91(2):pages 183--203, 1997.

Digital Library

Google Scholar

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Wu HSun MMi PTatti NNorth CRamakrishnan N(2018)Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy ModelsACM Transactions on Knowledge Discovery from Data10.1145/304701712:1(1-34)Online publication date: 31-Jan-2018
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Index Terms

Algorithms for storytelling
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Zabala-López ALinares-Vásquez MHaiduc SDonoso Y(2024)A Survey of data-centric technologies supporting decision-making before deploying military assetsDefence Technology10.1016/j.dt.2024.07.012Online publication date: Jul-2024
https://doi.org/10.1016/j.dt.2024.07.012
Wu HSun MMi PTatti NNorth CRamakrishnan N(2018)Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy ModelsACM Transactions on Knowledge Discovery from Data10.1145/304701712:1(1-34)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1145/3047017
Kader MBoedihardjo AHossain M(2018)F2ConText: how to extract holistic contexts of persons of interest for enhancing exploratory analysisKnowledge and Information Systems10.1007/s10115-018-1304-9Online publication date: 17-Dec-2018
https://doi.org/10.1007/s10115-018-1304-9
Ahmed AAlhashmi S(2015)A metric for Literature-Based Discovery methodology evaluation2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA)10.1109/AICCSA.2015.7507092(1-5)Online publication date: Nov-2015
https://doi.org/10.1109/AICCSA.2015.7507092
Endert AHossain MRamakrishnan NNorth CFiaux PAndrews C(2014)The human is the loopJournal of Intelligent Information Systems10.1007/s10844-014-0304-943:3(411-435)Online publication date: 1-Dec-2014
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Shahaf DGuestrin CHorvitz E(2013)"Metro maps of information" by Dafna Shahaf, Carlos Guestrin and Eric Horvitz, with Ching-man Au Yeung as coordinatorACM SIGWEB Newsletter10.1145/2451836.24518402013:Spring(1-9)Online publication date: 1-Apr-2013
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Shahaf DGuestrin C(2012)Connecting Two (or Less) DotsACM Transactions on Knowledge Discovery from Data10.1145/2086737.20867445:4(1-31)Online publication date: 1-Feb-2012
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