Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
From Association to Classification: Inference Using Weight of Evidence
IEEE Transactions on Knowledge and Data Engineering
Business Rule Extraction from Legacy Code
COMPSAC '96 Proceedings of the 20th Conference on Computer Software and Applications
Medical informatics: computer applications in health care and biomedicine (Health informatics)
Medical informatics: computer applications in health care and biomedicine (Health informatics)
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Data mining is the process of automatic classification of cases based on data patterns obtained from a dataset. A number of algorithms have been developed and implemented to extract information and discover knowledge patterns that may be useful for decision support. Once these patterns are extracted they can be used for automatic classification of case mixes. Although research has been conducted using diverse algorithms on small datasets (of about ten to fifteen attributes), in this paper, for the first time, we make use of a large database, namely, the interRAI Minimum Data Set for Mental Health (MDS-MH) containing over 455 attributes. Several data mining algorithms were used to classify clinical cases using MDS-MH data. The algorithms included Bayes methods, association rules, decision trees, and clustering. We compare these algorithms in various classification tasks and identify advantages and disadvantages of each algorithm with respect to their accuracy and use.