Discovering Interesting Association Rules in Medical Data

We are presently exploring the idea of discovering association rules in medical data. There are several technical aspects which make this problem challenging. In our case medical data sets are small, but have high dimensionality. Information content is rich: there exist numerical, categorical, time and even image attributes. Data records are generally noisy. We explain how to map medical data to a transaction format suitable for mining rules. The combinatorial nature of association rules matches our needs, but current algorithms are unsuitable for our purpose. We thereby introduce an improved algorithm to discover association rules in medical data which incorporates several important constraints. Some interesting results obtained by our program are discussed and we explain how the program parameters were set. We believe many of the problems we come across are likely to appear in other domains.