Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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In this paper, we propose a new algorithm named Inverted Hashing and Pruning (IHP) for mining association rules between words in text databases. The characteristics of text databases are quite different from those of retail transaction databases, and existing mining algorithms cannot handle text databases efficiently, because of the large number of itemsets (i.e., words) that need to be counted. Two well-known mining algorithms, the Apriori algorithm [1] and Direct Hashing and Pruning (DHP) algorithm [5], are evaluated in the context of mining text databases, and are compared with the proposed IHP algorithm. It has been shown that the IHP algorithm has better performance for large text databases.