DMTree: A Novel Indexing Method for Finding Similarities in Large Vector Sets

In a vector set, to find similarities we will compute distances from the querying vector to all other vectors. On a large vector set, computing too many distances as above takes a lot of time. So we need to find a way to compute less distance and the MTree structure is the technique we need. The MTr...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (4)
Main Authors: Do, Phuc, Phan, Trung, Duong, Huong
Format: Article
Language:English
Subjects:
Computer science
Distributed processing
Experiments
Indexing
Information technology
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Summary:In a vector set, to find similarities we will compute distances from the querying vector to all other vectors. On a large vector set, computing too many distances as above takes a lot of time. So we need to find a way to compute less distance and the MTree structure is the technique we need. The MTree structure is a technique of indexing vector sets based on a defined distance. We can solve effectively the problems of finding similarities by using the MTree structure. However, the MTree structure is built on one computer so the indexing power is limited. Today, large vector sets, not fit in one computer, are more and more. The MTree structure failed to index these large vector sets. Therefore, in this work, we present a novel indexing method, extended from the MTree structure, that can index large vector sets. Besides, we also perform experiments to prove the performance of this novel method.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110483