Evaluation of Partitional Algorithms for Clustering Medical Documents

There are large quantities of information about patients and their medical conditions. The discovery of trends and patterns hidden within the data could significantly enhance understanding of disease and medicine progression and management by evaluating stored medical documents. Methods are needed to facilitate discovering the trends and patterns within such large quantities of medical documents. Clustering medical documents into small number of meaningful clusters is one of these methods; because dealing with only the cluster that will contain relevant documents should improve effectiveness and efficiency. The produced clusters must be in high-quality because it will be used for further processing to discover the hidden trends and patterns. The focus of this paper is to experimentally evaluate the clusters’ quality of partitional clustering algorithms that use different criterion functions in the context of clustering medical documents. Our experimental results show that E1 leads to the best solution using repeated bisection as clustering method in term entropy. And I1 is the best using direct clustering methods in term of both entropy and purity. Keywords—Medical Documents, Partitional Algorithms, Document Clustering, Direct Clustering Method, Clusters’ Quality.