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Gridwave: a grid-based clustering algorithm for market transaction data based on spatial-temporal density-waves and synchronization.

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Title: Gridwave: a grid-based clustering algorithm for market transaction data based on spatial-temporal density-waves and synchronization.
Authors: Sun, Ruizhi1, Cai, Saihua1, Shi, Yinxue1, Deng, Chao1,2, Song, Jinwei3
Source: Multimedia Tools & Applications. Nov2018, Vol. 77 Issue 22, p29623-29637. 15p.
Subjects: Data mining, Parallel programs (Computer programs), Cluster analysis (Statistics), Communication network analysis, Synchronization
Abstract: The notion of density has been widely used in many spatial-temporal (ST) clustering methods. This paper proposes the novel notion of an ST density-wave, which is an extension of the notion of density. It also presents a new grid-based ST clustering algorithm called Gridwave based on the notion of ST density-waves and ST synchronization. The proposed algorithm can be used to discover synchronized changes in density among various locations as well as distinguish ST events and noise from market transaction data. Based on the theory of small-world networks, our algorithm can be used to evaluate ST synchronized correlations among regions with respective to the ST density over the whole network. To improve its performance, the proposed algorithm was implemented using parallel computing. To verify its feasibility, a real large-scale market transaction dataset was used to demonstrate the ST synchronized correlations and the final clustering results. Although our algorithm is applied in a domain-specific case, we suggest that the clustering notion and method could be generalized for other domain applications with similar ST data. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The notion of density has been widely used in many spatial-temporal (ST) clustering methods. This paper proposes the novel notion of an ST density-wave, which is an extension of the notion of density. It also presents a new grid-based ST clustering algorithm called Gridwave based on the notion of ST density-waves and ST synchronization. The proposed algorithm can be used to discover synchronized changes in density among various locations as well as distinguish ST events and noise from market transaction data. Based on the theory of small-world networks, our algorithm can be used to evaluate ST synchronized correlations among regions with respective to the ST density over the whole network. To improve its performance, the proposed algorithm was implemented using parallel computing. To verify its feasibility, a real large-scale market transaction dataset was used to demonstrate the ST synchronized correlations and the final clustering results. Although our algorithm is applied in a domain-specific case, we suggest that the clustering notion and method could be generalized for other domain applications with similar ST data. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-017-5441-z
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        Text: English
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      – SubjectFull: Cluster analysis (Statistics)
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      – SubjectFull: Communication network analysis
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      – SubjectFull: Synchronization
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              Text: Nov2018
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