- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
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.
5. Conclusion and future work
With the continuous development of data mining technology, research in multiple data sources mining is becoming more imperative and important. It has a wide range of applications in the fields such as robotics, automation and intelligent system design. This has been and will continue to be a growing interest in the research community to develop more advanced data mining methods and architectures. This paper critically reviewed many useful methods to mine meaningful information and discover new knowledge from multiple data sources: (i) pattern analysis, (ii) multiple data source classification, (iii) multiple data source clustering, and (iv) multiple data source fusion. There are still several challenges in these three effective approaches, which need further research.