Trend directed learning: A case study
Misleading caused by low quality data is a well known problem in knowledge discovery in databases. Several techniques have been introduced to deal with the problem which include inexact learning strategies, such as rough set based approaches and probabilistic approaches. This paper presents an approach for detecting trend using contribution functions. A trend directed method for the discovery of knowledge structure from low quality data bases is described. The experimental results show that trend directed methods are superior to other learning strategies, particularly when the learning is performed on low quality data bases.
KeywordsKnowledge discovery noisy data trend detection data mining machine learning inexact learning knowledge acquisition
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