Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its application to the transaction (or document) classification and clustering. However, most of the frequent-itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to identify a subset of the most promising ones that can be used for clustering. In this paper, we study how to directly find a subset of high quality frequent itemsets that can be used as a concise summary of the transaction database and to cluster the categorical data. By exploring some properties of the subset of itemsets that we are interested in, we proposed several search space pruning methods and designed an efficient algorithm called SUMMARY. Our empirical results have shown that SUMMARY runs very fast even when the minimum support is extremely low and scales very well with respect to the database size, and surprisingly, as a pure frequent itemset mining algorithm it is very effective in clustering the categorical data and summarizing the dense transaction databases.