Generalization and decision tree induction: efficient classification in data mining

Micheline Kamber; Lara Winstone; Wan Gong; Shan Cheng; Jiawei Han

Generalization and decision tree induction: efficient classification in data mining

Micheline Kamber, Lara Winstone, Wan Gong, Shan Cheng, Jiawei Han

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

Abstract

Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. This paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute-oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high-quality, multiple-level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.

Original language	English (US)
Title of host publication	Proceedings of the IEEE International Workshop on Research Issues in Data Engineering
Editors	P. Scheuermann
Publisher	IEEE
Pages	111-120
Number of pages	10
State	Published - 1997
Externally published	Yes
Event	Proceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97 - Birmingham, UK Duration: Apr 7 1997 → Apr 8 1997

Other

Other	Proceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97
City	Birmingham, UK
Period	4/7/97 → 4/8/97

ASJC Scopus subject areas

Hardware and Architecture
Software
Engineering (miscellaneous)

Library availability

Discover UIUC Full Text

Cite this

Kamber, M, Winstone, L, Gong, W, Cheng, S & Han, J 1997, Generalization and decision tree induction: efficient classification in data mining. in P Scheuermann (ed.), Proceedings of the IEEE International Workshop on Research Issues in Data Engineering. IEEE, pp. 111-120, Proceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97, Birmingham, UK, 4/7/97.

@inproceedings{72ad7729606e4bb99e1f45c03a3fd314,

title = "Generalization and decision tree induction: efficient classification in data mining",

abstract = "Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. This paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute-oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high-quality, multiple-level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.",

author = "Micheline Kamber and Lara Winstone and Wan Gong and Shan Cheng and Jiawei Han",

year = "1997",

language = "English (US)",

pages = "111--120",

editor = "P. Scheuermann",

booktitle = "Proceedings of the IEEE International Workshop on Research Issues in Data Engineering",

publisher = "IEEE",

note = "Proceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97 ; Conference date: 07-04-1997 Through 08-04-1997",

}

TY - GEN

T1 - Generalization and decision tree induction

T2 - Proceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97

AU - Kamber, Micheline

AU - Winstone, Lara

AU - Gong, Wan

AU - Cheng, Shan

AU - Han, Jiawei

PY - 1997

Y1 - 1997

N2 - Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. This paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute-oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high-quality, multiple-level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.

AB - Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. This paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute-oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high-quality, multiple-level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.

UR - http://www.scopus.com/inward/record.url?scp=0030651253&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030651253&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030651253

SP - 111

EP - 120

BT - Proceedings of the IEEE International Workshop on Research Issues in Data Engineering

A2 - Scheuermann, P.

PB - IEEE

Y2 - 7 April 1997 through 8 April 1997

ER -

Generalization and decision tree induction: efficient classification in data mining

Abstract

Other

ASJC Scopus subject areas

Library availability

Related links

Fingerprint

Cite this