Pattern Based Sequence Classification
Abstract— Pattern Based Sequence Classification. Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a < Final Year Projects 2016 > of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability.
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