It is a set of heuristics and computational methods to define the optimal parameters for creating the mining model from the spatial data that can be applied to the entire dataset to extract actionable patterns and detailed statistics.
Published in Chapter:
The Integral of Spatial Data Mining in the Era of Big Data: Algorithms and Applications
Gebeyehu Belay Gebremeskel (Chongqing University, China), Chai Yi (Chongqing University, China), and Zhongshi He (Chongqing University, China)
Copyright: © 2017
|Pages: 37
DOI: 10.4018/978-1-5225-2031-3.ch006
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.