Extraction of Meaningful Rules in a Medical Database

Clustering enhances the value of existing databases by revealing rules in the data. These rules are useful for understanding trends, making predictions of future events from historical data, or synthesizing data records into meaningful clusters. Through clustering are similar data items grouped together to form clusters. Clustering algorithms usually employ a distance metric based (e.g., Euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we study clustering algorithms for data with categorical attributes. Instead of using traditional clustering algorithms that use distances between points for clustering which is not an appropriate concept for Boolean and categorical attributes, we propose a novel concept of HAC (hierarchy of attributes and concepts) to measure the similarity/proximity between a pair of data points. In this study, HAC will be used as an aid to represent medical domain knowledge substructures to simplify the generation process of the databases through clustering. As a result, the research will identify interesting relationships and patterns among the data, and represent them in the form of association rules.