PROJECT TITLE :

Pattern Based Sequence Classification

ABSTRACT:

Sequence classification is an important task in information mining. We address the matter of sequence classification using rules composed of fascinating patterns found in a dataset of labelled sequences and accompanying category labels. We have a tendency to measure the interestingness of a pattern in a given category of sequences by combining the cohesion and also the support of the pattern. We tend to use the discovered patterns to get confident classification rules, and present 2 different ways in which of building a classifier. The primary classifier is predicated on an improved version of the prevailing methodology of classification based mostly on association rules, while the second ranks the foundations by initial measuring their worth specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we have a tendency to test a number of pattern feature based mostly models that use different types of patterns as options to represent every sequence as a feature vector. We tend to then apply a selection of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.


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