(Enter summary)
Abstract: . Classification is an important problem in the emerging field
of data mining. Although classification has been studied extensively in
the past, most of the classification algorithms are designed only for
memory-resident data, thus limiting their suitability for data mining
large data sets. This paper discusses issues in building a scalable classifier
and presents the design of SLIQ
1
, a new classifier. SLIQ is a decision
tree classifier that can handle both numeric and categorical... (Update)
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BibTeX entry: (Update)
M. Mehta, R. Agrawal, J. Rissanen. SLIQ: A fast scalable classifier for data mining. In 5th Intl. Conf. on Extending Database Technology, March 1996. http://citeseer.ist.psu.edu/mehta96sliq.html More
@inproceedings{ mehta96sliq,
author = "Manish Mehta and Rakesh Agrawal and Jorma Rissanen",
title = "{SLIQ}: A Fast Scalable Classifier for Data Mining",
booktitle = "Extending Database Technology",
pages = "18-32",
year = "1996",
url = "citeseer.ist.psu.edu/mehta96sliq.html" }
Citations (may not include all citations):
2177
Programs for Machine Learning (context) - Quinlan - 1993
1262
Classification and Regression Trees (context) - Breiman - 1984
417
Stochastic Complexity in Statistical Inquiry (context) - Rissanen - 1989
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Neural and Statistical Classification (context) - Michie, Spiegelhalter et al. - 1994
185
Inferring decision trees using minimum description length pr.. (context) - Quinlan, Rivest - 1989
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Computer Systems that Learn: Classification and Prediction M.. (context) - Weiss, Kulikowski - 1991
100
Database mining: A performance perspective
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An interval classifier for database mining applications
- Agrawal - 1992 ACM DBLP
62
Megainduction: Machine Learning on Very Large Databases (context) - Catlett - 1991
59
Coding decision trees (context) - Wallace, Patrick - 1993 ACM DBLP
54
Meta-learning for multistrategy and parallel learning (context) - Chan, Stolfo - 1993
35
MDL-based decision tree pruning
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1
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