Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39962
Title: IntelliClean: A knowledge-based intelligent data cleaner
Authors: Lee, M.L. 
Ling, T.W. 
Low, W.L.
Issue Date: 2000
Source: Lee, M.L.,Ling, T.W.,Low, W.L. (2000). IntelliClean: A knowledge-based intelligent data cleaner. Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 290-294. ScholarBank@NUS Repository.
Abstract: Existing data cleaning methods work on the basis of computing the degree of similarity between nearby records in a sorted database. High recall is achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision is achieved analogously at the cost of lower recall. This is the recall-precision dilemma. In this paper, we propose a generic knowledge-based framework for effective data cleaning that implements existing cleaning strategies and more. We develop a new method to compute transitive closure under uncertainty which handles the merging of groups of inexact duplicate records. Experimental results show that this framework can identify duplicates and anomalies with high recall and precision.
Source Title: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/39962
ISBN: 1581132336
Appears in Collections:Staff Publications

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