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The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications

The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications

Gebeyehu Belay Gebremeskel, Chai Yi, Zhongshi He
Copyright: © 2017 |Pages: 37
ISBN13: 9781522520313|ISBN10: 1522520317|EISBN13: 9781522520320
DOI: 10.4018/978-1-5225-2031-3.ch006
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MLA

Gebremeskel, Gebeyehu Belay, et al. "The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications." Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence, edited by Shrawan Kumar Trivedi, et al., IGI Global, 2017, pp. 90-126. https://doi.org/10.4018/978-1-5225-2031-3.ch006

APA

Gebremeskel, G. B., Yi, C., & He, Z. (2017). The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications. In S. Trivedi, S. Dey, A. Kumar, & T. Panda (Eds.), Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence (pp. 90-126). IGI Global. https://doi.org/10.4018/978-1-5225-2031-3.ch006

Chicago

Gebremeskel, Gebeyehu Belay, Chai Yi, and Zhongshi He. "The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications." In Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence, edited by Shrawan Kumar Trivedi, et al., 90-126. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2031-3.ch006

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Abstract

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

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