Proceedings ArticleDOI
Ordinal association rules for error identification in data sets
Andrian Marcus,Jonathan I. Maletic,King-Ip Lin +2 more
- pp 589-591
TLDR
A method that finds these rules and identifies potential errors in data is proposed, and one use for ordinal rules is to identify possible errors inData.Abstract:
A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.read more
Citations
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Journal ArticleDOI
Software defect prediction using relational association rule mining
TL;DR: This paper proposes a novel classification model based on relational association rules mining that overperforms, for most of the considered evaluation measures, the existing machine learning based techniques for defect prediction.
Patent
Semantic Discovery and Mapping Between Data Sources
Alexander Gorelik,Lingling Yan +1 more
TL;DR: In this paper, an apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems.
Journal ArticleDOI
The pairwise attribute noise detection algorithm
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Discovering transformations applied to a source table to generate a target table
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Book ChapterDOI
Data Cleansing: A Prelude to Knowledge Discovery
TL;DR: This chapter analyzes the problem of data cleansing and the identification of potential errors in data sets and presents a set of general methods that can be used to address the problem.
References
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