Looking at the Class Associative Classification Training Algorithm

Fadi Thabtah; Qazafi Mahmood; Lee McCluskey

doi:10.1109/ITNG.2008.250

Looking at the Class Associative Classification Training Algorithm

Fadi Thabtah, Qazafi Mahmood, Lee McCluskey

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

Abstract

Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardless of their class labels, our method joins only itemsets with similar class labels during the training phase. This prevents the accumulation of too many unnecessary merging during learning, and consequently results in huge saving (58%-91%) with reference of computational time and memory on large datasets.

Original language	English
Title of host publication	Proceedings of the Fifth International Conference on Information Technology
Subtitle of host publication	New Generations - ITNG 2008
Editors	Shahram Latifi
Publisher	IEEE
Pages	426-431
Number of pages	6
ISBN (Print)	0769530990, 9780769530994
DOIs	https://doi.org/10.1109/ITNG.2008.250
Publication status	Published - 18 Apr 2008
Event	International Conference on Information Technology: New Generations - Las Vegas, United States Duration: 7 Apr 2008 → 9 Apr 2008 http://www.itng.info/OLD/2008Web/

Conference

Conference	International Conference on Information Technology
Abbreviated title	ITNG 2008
Country/Territory	United States
City	Las Vegas
Period	7/04/08 → 9/04/08
Internet address	http://www.itng.info/OLD/2008Web/

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title = "Looking at the Class Associative Classification Training Algorithm",

abstract = "Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardless of their class labels, our method joins only itemsets with similar class labels during the training phase. This prevents the accumulation of too many unnecessary merging during learning, and consequently results in huge saving (58%-91%) with reference of computational time and memory on large datasets.",

keywords = "Data mining, Itemset, Merging, Rule discovery, Training phase",

author = "Fadi Thabtah and Qazafi Mahmood and Lee McCluskey",

year = "2008",

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doi = "10.1109/ITNG.2008.250",

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editor = "Shahram Latifi",

booktitle = "Proceedings of the Fifth International Conference on Information Technology",

publisher = "IEEE",

address = "United States",

note = "International Conference on Information Technology : New Generations, ITNG 2008 ; Conference date: 07-04-2008 Through 09-04-2008",

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Thabtah, F, Mahmood, Q & McCluskey, L 2008, Looking at the Class Associative Classification Training Algorithm. in S Latifi (ed.), Proceedings of the Fifth International Conference on Information Technology: New Generations - ITNG 2008., 4492517, IEEE, pp. 426-431, International Conference on Information Technology, Las Vegas, Nevada, United States, 7/04/08. https://doi.org/10.1109/ITNG.2008.250

Looking at the Class Associative Classification Training Algorithm. / Thabtah, Fadi; Mahmood, Qazafi; McCluskey, Lee.

Proceedings of the Fifth International Conference on Information Technology: New Generations - ITNG 2008. ed. / Shahram Latifi. IEEE, 2008. p. 426-431 4492517.

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

TY - GEN

T1 - Looking at the Class Associative Classification Training Algorithm

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N2 - Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardless of their class labels, our method joins only itemsets with similar class labels during the training phase. This prevents the accumulation of too many unnecessary merging during learning, and consequently results in huge saving (58%-91%) with reference of computational time and memory on large datasets.

AB - Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardless of their class labels, our method joins only itemsets with similar class labels during the training phase. This prevents the accumulation of too many unnecessary merging during learning, and consequently results in huge saving (58%-91%) with reference of computational time and memory on large datasets.

KW - Data mining

KW - Itemset

KW - Merging

KW - Rule discovery

KW - Training phase

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BT - Proceedings of the Fifth International Conference on Information Technology

A2 - Latifi, Shahram

PB - IEEE

T2 - International Conference on Information Technology

Y2 - 7 April 2008 through 9 April 2008

ER -

Looking at the Class Associative Classification Training Algorithm

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