Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
A vector space model for automatic indexing
Communications of the ACM
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining of Massive Datasets
Efficient determination of binary non-negative vector neighbors with regard to cosine similarity
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Using Non-Zero Dimensions for the Cosine and Tanimoto Similarity Search Among Real Valued Vectors
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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The Tanimoto similarity measure finds numerous applications in chemistry, bio-informatics, information retrieval and text mining. A typical task in these applications is finding most similar vectors. The task is very time consuming in the case of very large data sets. Thus methods that allow for efficient restriction of the number of vectors that have a chance to be sufficiently similar to a given vector are of high importance. To this end, recently, we have derived bounds on lengths of vectors similar with respect to the Tanimoto similarity. In this paper, we recall those results and derive new bounds on lengths of real valued vectors that have a chance to be Tanimoto similar to a given vector in a required degree. Finally, we compare the previous and current results and illustrate their usefulness.