Abstract
Categorization is one of the major tasks in the field of traffic network. In comparison to the other techniques which can be correlated with the categorization tries to find out all the frequent blueprints which existed in the input categorical of the traffic network satisfying the public specified least support or inequity measures like minimum confidence or information gain. Those blueprints are used shortly either as rules for rule-based classifier or training features for Support Vector Machine (SVM) categorizer. Several algorithms have been proposed to solve the categorization problem on uncertain conditions in the road transportation due to improper traffic network. In this research paper we have proposed an innovative algorithm concord which extracts discriminative patterns directly and effectively from the uncertain traffic network conditions as classified in the features which assists everyone to use the road transportation in traveling quick and safe to any destinations.
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