A Locality Sensitive Hashing Technique for Categorical Data

Author:

Lee Kyung Mi1,Lee Keon Myung1

Affiliation:

1. Chungbuk National University

Abstract

The measured data may contain various types of attributes such as continuous, categorical, and set-valued attributes. Several locality-sensitive hashing techniques, which enable to find similar pairs of data in a fast and approximate way, have been developed for data with either numeric or set-valued attributes. This paper introduces a new locality sensitive-hashing technique applicable to data with categorical attributes.

Publisher

Trans Tech Publications, Ltd.

Reference14 articles.

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3. U. Manber: Finding similar files in a large file system, Proc. USENIX Conference (1994) 1–10.

4. A. Z. Broder: On the resemblance and containment of documents, Proc. Compression and Complexity of Sequence (1997) 21–29.

5. A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher: Min-wise independent permutations, ACM Symposium on Theory of Computing (1998) 327–336.

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