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Review on mining data from multiple data sources
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DOI：10.1016/j.patrec.2018.01.013.png)
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Abstract
En 中文
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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Multiple data source mining
Pattern analysis
Data classification
Data clustering
Data fusion
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