Algorithms for clustering data
Algorithms for clustering data
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Concept Formation in WWTP by Means of Classification Techniques: ACompared Study
Applied Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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Using domain knowledge in unsupervised learning has shown to be a useful strategy when the set of examples of a given domain has not an evident structure or presents some level of noise. This background knowledge can be expressed as a set of classification rules and introduced as a semantic bias during the learning process. In this work we present some experiments on the use of partial domain knowledge in conceptual clustering. The domain knowledge (or domain theory) is used to select a set of examples that will be used to start the learning process, this knowledge has not to be complete neither consistent. This bias will increase the quality of the final groups and reduce the effect of the order of the examples. Some measures of stability of classification are used as evaluation method. The improvement of the acquired concepts can be used to improve and correct the domain knowledge. A set of heuristics to revise the original domain theory has been experimented, yielding to some interesting results.