Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6940327 | Pattern Recognition Letters | 2018 | 9 Pages |
Abstract
In this paper, we review recent progresses in the area of mining data from multiple data sources. The advancement of information communication technology has generated a large amount of data from different sources, which may be stored in different geological locations. Mining data from multiple data sources to extract useful information is considered to be a very challenging task in the field of data mining, especially in the current big data era. The methods of mining multiple data sources can be divided mainly into four groups: (i) pattern analysis, (ii) multiple data source classification, (iii) multiple data source clustering, and (iv) multiple data source fusion. The main purpose of this review is to systematically explore the ideas behind current multiple data source mining methods and to consolidate recent research results in this field.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Ruili Wang, Wanting Ji, Mingzhe Liu, Xun Wang, Jian Weng, Song Deng, Suying Gao, Chang-an Yuan,