Journal of Information Processing
Online ISSN : 1882-6652
Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework
Masafumi OyamadaJianquan LiuShinji ItoKazuyo NaritaTakuya ArakiHiroyuki Kitagawa
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2018 Volume 26 Pages 416-426


In this paper, we present CVS (Compressed Vector Set), a fast and space-efficient data mining framework that efficiently handles both sparse and dense datasets. CVS holds a set of vectors in a compressed format and conducts primitive vector operations, such as lp-norm and dot product, without decompression. By combining these primitive operations, CVS accelerates prominent data mining or machine learning algorithms including k-nearest neighbor algorithm, stochastic gradient descent algorithm on logistic regression, and kernel methods. In contrast to the commonly used sparse matrix/vector representation, which is not effective for dense datasets, CVS efficiently handles sparse datasets and dense datasets in a unified manner. Our experimental results demonstrate that CVS can process both dense datasets and sparse datasets faster than conventional sparse vector representation with smaller memory usage.

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© 2018 by the Information Processing Society of Japan
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