DMCA
K.: Abstraction of High Level Concepts from Numerical Values in Databases (1994)
| Venue: | In: Proceedings of the Knowledge Discovery in Data Mining Workshop (KDD |
| Citations: | 25 - 13 self |
BibTeX
@INPROCEEDINGS{Chu94k.:abstraction,
author = {Wesley W. Chu and Kuorong Chiang T},
title = {K.: Abstraction of High Level Concepts from Numerical Values in Databases},
booktitle = {In: Proceedings of the Knowledge Discovery in Data Mining Workshop (KDD},
year = {1994},
pages = {133--144}
}
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Abstract
A conceptual clustering method is proposed for discovering high level concepts of numerical attribute values from databases. The method considers both frequency and value distributions of data, thus is able to discover relevant concepts from numerical attributes. The discovered knowledge can be used for representing data semantically and for providing approximate answers when exact ones are not available. Our knowledge discovery approach is to partition the data set of one or more attributes into clusters that minimize the relaxation error. An algorithm is developed which finds the best binary partition in O(n) time and generates a concept hierarchy in O(n2) time where n is the number of distinct values of the attribute. The effectiveness of our clustering method is demonstrated by applying it to a large transportation database for approximate query answering. Key words: approximate query answering, type abstraction hierarchy, conceptual clustering, discretization, data summarization, knowledge discovery in databases. 1
Keyphrases
high level concept numerical value approximate query answering exact one conceptual clustering method distinct value numerical attribute value knowledge discovery approach data summarization approximate answer conceptual clustering binary partition type abstraction hierarchy relevant concept large transportation database concept hierarchy value distribution numerical attribute relaxation error key word data set knowledge discovery discovered knowledge
