Abstract
Associative classification is a field of data mining that deals with the mining of the associations among different data variables and further classifying these variables. There are various techniques, explored by different researchers to classify association rules. Most of these classification techniques use confidence interest measure in scrutiny and ranking the rules. But confidence measure itself produces many times inaccurate results as investigated in various literature. So, in this paper, classifier based on a recent interest measure named Significant Rule Power Factor (SRPF) has been explored. The experiments are conducted through implementation in Weka tool. Results show that SRPF-based classifier is a new-generation classifier with light in weight and better results in accuracy.
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Sharma, O., Kumar, S., Joshi, N. (2019). SRPF Interest Measure Based Classification to Extract Important Patterns. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_50
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DOI: https://doi.org/10.1007/978-981-13-1217-5_50
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