Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Scalable Model-based Clustering by Working on Data Summaries
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Clinical and financial outcomes analysis with existing hospital patient records
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Scalable model-based cluster analysis using clustering features
Pattern Recognition
Uniqueness of medical data mining
Artificial Intelligence in Medicine
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In this paper, we explore data mining techniques for the task of identifying and describing risk groups for colorectal cancer (CRC) from population based administrative health data. Association rule discovery, association classification and scalable clustering analysis are applied to the colorectal cancer patients’ profiles in contrast to background patients’ profiles. These data mining methods enable us to identify the most common characteristics of the colorectal cancer patients. The knowledge discovered by data mining methods which are quite different from traditional survey approaches. Although it is heuristic, the data mining methods may identify risk groups for further epidemiological study, such as older patients living near health facilities yet seldom utilising those facilities, and with respiratory and circulatory diseases.