Incremental mining of association patterns on compressed data

Vincent To Yee Ng; Jacky Man Lee Wong; Paul Bao

Incremental mining of association patterns on compressed data

Vincent To Yee Ng, Jacky Man Lee Wong, Paul Bao

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

Introducing data compression concept to large databases has been proposed for many years. In this project, we propose a new algorithm for the compression of large databases. Our goal is to optimize the I/O effort for finding association rules. The algorithm partitions the databases into two parts and all transactions will be compressed with the help of a reference transaction found in the small partition. We also compared the proposed compression algorithms with a normal compression algorithm - the binary compression. Empirical evaluation shows that the proposed algorithm performs well both in reducing the storage space and the I/O process required to find the large itemsets for association rules.

Original language	English
Title of host publication	Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
Pages	441-446
Number of pages	6
Publication status	Published - 1 Dec 2001
Event	Joint 9th IFSA World Congress and 20th NAFIPS International Conference - Vancouver, BC, Canada Duration: 25 Jul 2001 → 28 Jul 2001

Conference

Conference	Joint 9th IFSA World Congress and 20th NAFIPS International Conference
Country/Territory	Canada
City	Vancouver, BC
Period	25/07/01 → 28/07/01

ASJC Scopus subject areas

General Computer Science
General Mathematics

Cite this

@inproceedings{9c1b2d279644421cbe24fb8ab4a49d04,

title = "Incremental mining of association patterns on compressed data",

abstract = "Introducing data compression concept to large databases has been proposed for many years. In this project, we propose a new algorithm for the compression of large databases. Our goal is to optimize the I/O effort for finding association rules. The algorithm partitions the databases into two parts and all transactions will be compressed with the help of a reference transaction found in the small partition. We also compared the proposed compression algorithms with a normal compression algorithm - the binary compression. Empirical evaluation shows that the proposed algorithm performs well both in reducing the storage space and the I/O process required to find the large itemsets for association rules.",

author = "Ng, {Vincent To Yee} and Wong, {Jacky Man Lee} and Paul Bao",

year = "2001",

month = dec,

day = "1",

language = "English",

pages = "441--446",

booktitle = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",

note = "Joint 9th IFSA World Congress and 20th NAFIPS International Conference ; Conference date: 25-07-2001 Through 28-07-2001",

}

Incremental mining of association patterns on compressed data. / Ng, Vincent To Yee; Wong, Jacky Man Lee; Bao, Paul.
Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS. 2001. p. 441-446.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Incremental mining of association patterns on compressed data

AU - Ng, Vincent To Yee

AU - Wong, Jacky Man Lee

AU - Bao, Paul

PY - 2001/12/1

Y1 - 2001/12/1

N2 - Introducing data compression concept to large databases has been proposed for many years. In this project, we propose a new algorithm for the compression of large databases. Our goal is to optimize the I/O effort for finding association rules. The algorithm partitions the databases into two parts and all transactions will be compressed with the help of a reference transaction found in the small partition. We also compared the proposed compression algorithms with a normal compression algorithm - the binary compression. Empirical evaluation shows that the proposed algorithm performs well both in reducing the storage space and the I/O process required to find the large itemsets for association rules.

AB - Introducing data compression concept to large databases has been proposed for many years. In this project, we propose a new algorithm for the compression of large databases. Our goal is to optimize the I/O effort for finding association rules. The algorithm partitions the databases into two parts and all transactions will be compressed with the help of a reference transaction found in the small partition. We also compared the proposed compression algorithms with a normal compression algorithm - the binary compression. Empirical evaluation shows that the proposed algorithm performs well both in reducing the storage space and the I/O process required to find the large itemsets for association rules.

UR - http://www.scopus.com/inward/record.url?scp=0035792260&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SP - 441

EP - 446

BT - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

T2 - Joint 9th IFSA World Congress and 20th NAFIPS International Conference

Y2 - 25 July 2001 through 28 July 2001

ER -

Incremental mining of association patterns on compressed data

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this