Abstract
With the advance of Web techniques, individuals and organizations can make use of low-cost information and knowledge on the Internet when carrying out data mining for applications. However, information from different datasources is often untrustworthy, contradictory, fraudulent, and even potentially dangerous to applications. Therefore, the discovery of reliable knowledge from different data-sources (databases or datasets) has become a critical task in multi-database mining research. In this chapter, a data-source is taken as a knowledge base (From our local pattern analysis, this assumption is reasonable.). A framework is thus presented for identifying quality knowledge from different data-sources.
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Notes
For convenience, we also call a company a data-source in a knowledge sharing environment. This is because a company is taken as a data-source when the company’s knowledge is also shared by other companies.
The consistency is dealt with in Chapter 6. Therefore, we assume that the knowledge in data-sources is consistent for the time being.
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© 2004 Springer-Verlag London
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Zhang, S., Zhang, C., Wu, X. (2004). Identifying Quality Knowledge. In: Knowledge Discovery in Multiple Databases. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-388-6_4
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DOI: https://doi.org/10.1007/978-0-85729-388-6_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1050-7
Online ISBN: 978-0-85729-388-6
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