Abstract
Analyzing the temporal behavior of frequent patterns and decreasing the size of discovered patterns are two major challenges in the area of temporal data mining. Several methods are available in this context, among them, constraint based pattern mining approach contributed a lot in this field. There are several methods have been proposed in this direction. However, while exploring the patterns based on time granularities: cyclic and partial cyclic patterns, the existing methods use the traditional Apriori algorithm or Interleaved algorithm, that takes lots of time while generating candidates. In this paper, a new strategy – Frequent Pattern Growth technique Incorporated with Special Constraints (FPGSC) is proposed. Here, complete cyclic and partial cyclic constraints are imposed on the framework consists of a Frequent pattern growth method for generating frequent patterns. This algorithm is able to discover complete cyclic and partial cyclic patterns in an efficient way. We also analyze the experimental results that show that it is as efficient as other algorithms in this field and it is better to generate more appropriate temporal patterns.
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Upadhyay, P., Kohli, N., Pandey, M.K. (2019). Discovering Cyclic and Partial Cyclic Patterns Using the FP Growth Method Incorporated with Special Constraints. In: Prateek, M., Sharma, D., Tiwari, R., Sharma, R., Kumar, K., Kumar, N. (eds) Next Generation Computing Technologies on Computational Intelligence. NGCT 2018. Communications in Computer and Information Science, vol 922. Springer, Singapore. https://doi.org/10.1007/978-981-15-1718-1_19
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DOI: https://doi.org/10.1007/978-981-15-1718-1_19
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