Rakesh Agrawal Sakti Ghosh Tomasz Imielinski Bala Iyer Arun Swami IBM Almaden ...  Home/Search
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Abstract: We are given a large population database that contains information about population instances. The population is known to comprise of m groups, but the population instances are not labeled with the group identification. Also given is a population sample (much smaller than the population but representative of it) in which the group labels of the instances are known. We present an interval classifier (IC) which generates a classification function for each group that can be used to efficiently... (Update)
Context of citations to this paper: More
.... numerical attributes can be performed similarly but in a more automatic way by the examination of data distribution characteristics [1, 9, 5]. In many cases, it may not require any predefined concept hierarchies. For example, the household income of buyers can be clustered into...
.... and classification in numerous applications, including information retrieval [6, 7, 8, 26] facility location [11, 30] data mining [2, 28], spatial data bases [9, 20, 25] data compression [23] image processing [19, 27] astrophysics [22] and scientific computing [5]...
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BibTeX entry: (Update)
R. Agrawal, et al. An interval classifier for database mining applications. In VLDB Conference, Aug 1992. http://citeseer.ist.psu.edu/agrawal92interval.html More
@inproceedings{ agrawal92interval,
author = "Rakesh {Agrawal} and Sakti {Ghosh} and Tomasz {Imielinski} and Bala {Iyer} and Arun {Swami}",
title = "An Interval Classifier for Database Mining Applications",
booktitle = "Proceedings of the 18th International Conference on Very Large Databases",
publisher = "Morgan Kaufmann Publishers",
address = "San Francisco, U.S.A.",
editor = "Li-Yan {Yuan}",
isbn = "1-55860-151-1",
pages = "560--573",
year = "1992",
url = "citeseer.ist.psu.edu/agrawal92interval.html" }
Citations (may not include all citations):1359 Induction of Decision Trees (context) - Quinlan - 1986 ACM DBLP
1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
805 Algorithms for Clustering Data (context) - Jain, Dube - 1988 ACM
227 An Introduction to Computing with Neural Nets (context) - Lippmann - 1987 ACM
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162 Simplifying Decision Trees - Quinlan - 1987 ACM DBLP
160 Knowledge Discovery in Databases (context) - Piatetsky-Shapiro - 1991 ACM DBLP
94 Constructing Optimal Binary Decision Trees is NP-Complete (context) - Hayafil, Rivest - 1976 DBLP
80 Benchmarking Database Systems: A Systematic Approach - Bitton, DeWitt et al. - 1983
70 Graphical Methods of Data Analysis (context) - Chambers, Cleaveland et al. - 1983
31 Iowa State University Press (context) - Snedecor, Cochran et al. - 1980
14 Decision Trees in Pattern Recognition (context) - Dattatreya, Kanal - 1985
9 Discovery from Databases: A Review of AI and Statistical Tec.. (context) - Lubinsky - 1989
7 Practitioner Problems in Need of Database Research: Research.. (context) - Krishnamurthy, Imielinski - 1991
6 Data Dredging (context) - Tsur - 1990 ACM DBLP
5 Application of Information Theory to Pattern Recognition and.. (context) - Chou - 1988
4 NASA Ames Research Center (context) - Buntine, IND et al. - 1991
2 Wray Buntine and Matha Del Alto (context) - Buntine, Alto et al. - 1991
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