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WIREs Data Mining Knowl Discov
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A survey on mining multiple data sources

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Abstract Advancements in computer and communication technologies demand new perceptions of distributed computing environments and development of distributed data sources for storing voluminous amount of data. In such circumstances, mining multiple data sources for extracting useful patterns of significance is being considered as a challenging task within the data mining community. The domain, multi‐database mining (MDM) is regarded as a promising research area as evidenced by numerous research attempts in the recent past. The methods exist for discovering knowledge from multiple data sources, they fall into two wide categories, namely (1) mono‐database mining and (2) local pattern analysis. The main intent of the survey is to explain the idea behind those approaches and consolidate the research contributions along with their significance and limitations. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

Mono‐database mining.

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Local pattern analysis.

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Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Fundamental Concepts of Data and Knowledge > Data Concepts

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