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Deep data analyzing algorithm based on scale space theory

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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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Acknowledgments

This research is supported in part by Zhejiang Provincial Natural Science Foundation of China (No. LY16F020012) and Ningbo Key Laboratory of intelligent home appliances (No. 2016A22008).

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Authors and Affiliations

  1. Big Data Deep Analyzing Institute, Intelligent Electronic Institute, Zhejiang Business Technology Institute, No. 1988 Airport Road, Ningbo, China

    Yiwei Zhu & Kun Gao

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  1. Yiwei Zhu
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  2. Kun Gao
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Correspondence to Kun Gao.

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Zhu, Y., Gao, K. Deep data analyzing algorithm based on scale space theory. Cluster Comput 20, 1–11 (2017). https://doi.org/10.1007/s10586-016-0677-3

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  • DOI: https://doi.org/10.1007/s10586-016-0677-3

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