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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14220
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| Title: | KNOWLEDGE DISCOVERY FROM DATABASES: THE NYU PROJECT |
| Authors: | Clifford, James
Dhar, Vasant
Tuzhilin, Alex |
| Issue Date: | 15-Feb-1995 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-95-12 |
| Abstract: | More and more application domains, from financial market analysis to
weather prediction, from monitoring supermarket purchases to monitoring
satellite images, are becomingly increasingly data-intensive. The result
is massive databases that are growing at a rapid rate - it has been
estimated that the worldâs electronic data almost doubles every
year. With this rate of data explosion, there is a pressing need for
computers to play an increasing role in analyzing these huge data
repositories which are impossible to penetrate manually. The challenge
is to ferret out the regularities in the data that will prove to be
interesting to the user. A group in the Information Systems department
at the NYU Business School has been working in this area for a number of
years. The focus of our project is now on the discovery of patterns from
time series data. In this paper we give an overview of the kinds of
databases we are "miningâ and the kinds of temporal
patterns and rules which we are attempting to discover. In the first
phase of this research, we have developed a taxonomy of patterns as a
way to organize our research agenda. We wish to share the taxonomy with
the research community in the "knowledge discovery in
databases" area since we have found it useful in classifying the
universe of regularities or patterns into distinct types, that is,
patterns which differ in terms of their structure and the amount 6f
search effort required to find them. Although the primary focus of our
project is on time series data, and the examples we will present are
chosen from this arena, the taxonomy is general enough to apply to any
type of data. |
| URI: | http://hdl.handle.net/2451/14220 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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