Topics in 0--1 data

Ella Bingham; H. Mannila; J.K. Seppänen

doi:10.1145/775047.775112

Topics in 0--1 data

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

Abstract

Large 0--1 datasets arise in various applications, such as market basket analysis and information retrieval. We concentrate on the study of topic models, aiming at results which indicate why certain methods succeed or fail. We describe simple algorithms for finding topic models from 0--1 data. We give theoretical results showing that the algorithms can discover the epsilon-separable topic models of Papadimitriou et al. We present empirical results showing that the algorithms find natural topics in real-world data sets. We also briefly discuss the connections to matrix approaches, including nonnegative matrix factorization and independent component analysis.

Original language	English
Title of host publication	KDD '02: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	ACM
Pages	450-455
ISBN (Electronic)	978-1-58113-567-1
DOIs	https://doi.org/10.1145/775047.775112
Publication status	Published - 2002
MoE publication type	A4 Conference publication
Event	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Edmonton, Canada Duration: 23 Jun 2002 → 26 Jun 2002 Conference number: 8

Conference

Conference	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated title	KDD
Country/Territory	Canada
City	Edmonton
Period	23/06/2002 → 26/06/2002

Keywords

0-1 data
data mining
latent variable model
topic models

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10.1145/775047.775112

Cite this

@inproceedings{0dfbf81562974386b34fa543684c68e7,

title = "Topics in 0--1 data",

abstract = "Large 0--1 datasets arise in various applications, such as market basket analysis and information retrieval. We concentrate on the study of topic models, aiming at results which indicate why certain methods succeed or fail. We describe simple algorithms for finding topic models from 0--1 data. We give theoretical results showing that the algorithms can discover the epsilon-separable topic models of Papadimitriou et al. We present empirical results showing that the algorithms find natural topics in real-world data sets. We also briefly discuss the connections to matrix approaches, including nonnegative matrix factorization and independent component analysis.",

keywords = "0-1 data, data mining, latent variable model, topic models, 0-1 data, data mining, latent variable model, topic models, 0-1 data, data mining, latent variable model, topic models",

author = "Ella Bingham and H. Mannila and J.K. Sepp{\"a}nen",

year = "2002",

doi = "10.1145/775047.775112",

language = "English",

pages = "450--455",

booktitle = "KDD '02: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "ACM",

address = "United States",

note = "ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ; Conference date: 23-06-2002 Through 26-06-2002",

}

TY - GEN

T1 - Topics in 0--1 data

AU - Bingham, Ella

AU - Mannila, H.

AU - Seppänen, J.K.

N1 - Conference code: 8

PY - 2002

Y1 - 2002

N2 - Large 0--1 datasets arise in various applications, such as market basket analysis and information retrieval. We concentrate on the study of topic models, aiming at results which indicate why certain methods succeed or fail. We describe simple algorithms for finding topic models from 0--1 data. We give theoretical results showing that the algorithms can discover the epsilon-separable topic models of Papadimitriou et al. We present empirical results showing that the algorithms find natural topics in real-world data sets. We also briefly discuss the connections to matrix approaches, including nonnegative matrix factorization and independent component analysis.

AB - Large 0--1 datasets arise in various applications, such as market basket analysis and information retrieval. We concentrate on the study of topic models, aiming at results which indicate why certain methods succeed or fail. We describe simple algorithms for finding topic models from 0--1 data. We give theoretical results showing that the algorithms can discover the epsilon-separable topic models of Papadimitriou et al. We present empirical results showing that the algorithms find natural topics in real-world data sets. We also briefly discuss the connections to matrix approaches, including nonnegative matrix factorization and independent component analysis.

KW - 0-1 data

KW - data mining

KW - latent variable model

KW - topic models

KW - 0-1 data

KW - data mining

KW - latent variable model

KW - topic models

KW - 0-1 data

KW - data mining

KW - latent variable model

KW - topic models

U2 - 10.1145/775047.775112

DO - 10.1145/775047.775112

M3 - Conference article in proceedings

SP - 450

EP - 455

BT - KDD '02: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - ACM

T2 - ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Y2 - 23 June 2002 through 26 June 2002

ER -

Topics in 0--1 data

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Keywords

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