document: Efficient discovery of error-tolerant frequent itemsets in high dimensions We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition . We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data ( customer-purchase data , web browsing data , text , etc. ) . The algorithm exploits sparseness of the underlying data to find large groups of items that are correlated over database records ( rows ) . The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data . We evaluate the new algorithm on three real-world applications : clustering high-dimensional data , query selectivity estimation and collaborative filtering . Results show that the algorithm consistently uncovers structure in large sparse databases that other traditional clustering algorithms fail to find .
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