In this paper, we formulate the problem of summarization of a dataset of transactions with categorical attributes as an optimization problem involving two objective functions - compaction gain and information loss. We propose metrics to characterize the output of any summarization algorithm. We investigate two approaches to address this problem. The first approach is an adaptation of clustering and the second approach makes use of frequent itemsets from the association analysis domain. We illustrate one application of summarization in the field of network data where we show how our technique can be effectively used to summarize network traffic into a compact but meaningful representation. Specifically, we evaluate our proposed algorithms on the 1998 DARPA Off-line Intrusion Detection Evaluation data and network data generated by SKAION Corp for the ARDA information assurance program.