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In data mining post-processing, rule selection using objective rule evaluation indices is one of a useful method to find out valuable knowledge from mined patterns. However, the relationship between an index value and experts' criteria has never been clarified. In this study, we have compared the accuracies of classification learning algorithms for datasets with randomized class distributions and...
This paper presents a novel approach to feature construction for structured data in order to enhance graph prediction classification performance. To this end we combine graph mining techniques with graph kernel based classifiers. The main idea is to employ efficient mining techniques to extract a set of patterns correlated with the target concept and use these, or a selected subset of these, to annotate...
In this paper we present a new algorithm for semisupervised clustering. We assume to have a small set of labeled samples and we use it in a clustering algorithm to discover relevant patterns. We study how our algorithm works against two other semisupervised algorithms when the data are multimodal. Then, we study the case where the user is able to produce few samples for some classes but not for each...
For many data mining applications, it is necessary to develop algorithms that use unlabeled data to improve the accuracy of the supervised learning. Co-Training is a popular semi-supervised learning algorithm. It assumes that each example is represented by two or more redundantly sufficient sets of features (views) and these views are independent given the class. However, these assumptions are not...
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