Computation of term/document discrimination values by use of the cover coefficient
Journal of the American Society for Information Science
File organization and processing
File organization and processing
Partitioned signature files: design issues and performance evaluation
ACM Transactions on Information Systems (TOIS)
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases
ACM Transactions on Database Systems (TODS)
Dynamic partitioning of signature files
ACM Transactions on Information Systems (TOIS)
Optimal weight assignment for signature generation
ACM Transactions on Database Systems (TODS)
Optimal signature extraction and information loss
ACM Transactions on Database Systems (TODS)
An automatic and tunable document indexing system
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Signature files: an access method for documents and its analytical performance evaluation
ACM Transactions on Information Systems (TOIS)
HieNet: a user-centered approach for automatic link generation
HYPERTEXT '93 Proceedings of the fifth ACM conference on Hypertext
On the signature weight in “multiple” m signature files
ACM SIGIR Forum
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Our analysis combines the concerns of signature extraction and signature file organization which have usually been treated as separate issues. We also relax the uniform frequency and single term query assumptions and provide a comprehensive analysis for multiterm query environments where terms can be classified based on their query and database occurrence frequencies. The performance of three superimposed signature generation schemes is explored as they are applied to one dynamic signature file organization based on linear hashing: Linear Hashing with Superimposed Signatures (LHSS). First scheme (SM) allows all terms set the same number of bits regardless of their discriminatory power whereas the second and third methods (MMS and MMM) emphasize the terms with high query and low database occurrence frequencies. Of these three schemes, only MMM takes the probability distribution of the number of query terms into account in finding the optimal mapping strategy. Derivation of performance evaluation formulas is provided together with the results of various experimental settings. Suggestions as to how to implement the given techniques in real life cases are also provided. Results indicate that MMM outperforms the other methods as the gap between the discriminatory power of the terms gets larger. The absolute value of the savings provided by MMM reach a maximum for the high query weight case. However, the extra savings decline sharply for high weight and moderately for the low weight queries with the increase in database size.