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Published in: Cluster Computing 1/2017

12-11-2016

Deep data analyzing algorithm based on scale space theory

Authors: Yiwei Zhu, Kun Gao

Published in: Cluster Computing | Issue 1/2017

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Abstract

Scale space theory has been introduced into the field of big data, but its research is still not deep enough and perfect because of the lack of universal theory and method. With deepening of big data processing applications, the research becomes more and more urgent. In view of the above question, this paper studies pervasive multiscale data analysis theory and method, and proposes ARAMS (Association Rules Algorithm based on Multi Scale). On one hand, we give the definition and partition of data scale as well as the relationship of multiscale data set between the upper scale and lower scale based on concept hierarchy theory. On the other hand, we clarify the definition of multiscale data analysis, study essence and classification method. Previous studies show that the proposed method has high coverage rate, high accuracy rate, lower error rate of support estimation degree and greater improvement the efficiency than the traditional algorithm.

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Metadata
Title
Deep data analyzing algorithm based on scale space theory
Authors
Yiwei Zhu
Kun Gao
Publication date
12-11-2016
Publisher
Springer US
Published in
Cluster Computing / Issue 1/2017
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-016-0677-3

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