UMiner: A Data Mining System Handling Uncertainty and Quality
the revealing and handling of uncertainty in the context of data mining tasks. In traditional data mining systems database values are not overlapping and treated equally in the classification process. The different values in the database are classified in the available categories in a crisp manner i.e. they may be classified into at most one cluster. Also all the values that are classified in a cluster belong to it with the same degree of belief Thus, there is significant information included in classification results that is not exploited by the traditional classification approaches.
the evaluation of data mining results based on well-established quality criteria. Most of the clustering algorithms depend on assumptions and initial guesses in order to define the subgroups presented in a data set [TK99]. As a consequence, in most applications the final clustering scheme requires some sort of evaluation.
KeywordsData Mining Cluster Algorithm Validity Index Cluster Scheme Cluster Validity
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