Sequence classification is an efficient task in data mining. Sequence classification problem can be solved by rules that consist of interesting patterns. Another major problem in data mining is pattern mining. In pattern mining, patterns can be used as rules. These rules may be more accurate or simpler to understand while classifying the data object. The cohesion and support of the pattern are used to define interestingness of a pattern. The degree of interest in patterns in a given class of sequences can be measured by combining these two factors. The patterns found can be used to generate reliable classification rules. There are two different ways to build a classifier. The first classifier consists of advanced classification methods that rely on association rules. In the second classifier, the value belonging to the new data object is first measured then the rules are ranked. A well-known methods of association classification are CBA (Classification based on Association rules), CMAR (Classification based on Multiple class-Association Rules), and CPAR (Classification based on Predictive Association Rules) etc. mine the frequent and confident patterns for building a classifier. All these approaches do not consider the cohesion of a pattern and applicable to only one type of pattern. These limitations can be overcome by taking into account a cohesion factor to define interestingness of pattern and can consider another type of pattern.

Sequence classification, interesting patterns, classification rules

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