Frequent Pattern Data Mining Based Clustering and Classification Using Fuzzy Back Propagation Resnet Convolutional Networks

Authors

  • K. H. Niralgikar PHD,Research Scholar, CSPIT, CHARUSAT, CHANGA, Gujarat. Mechanical Department, CHARUSAT.CHANGA.
  • Mukesh A. Bulsara Professor Mechanical Department GCETV.V Nagar,Gujarat

Keywords:

Frequent pattern mining, data mining, sequence, clustering, deep learning

Abstract

Numerous scalable approaches are developed for mining various types of patterns, with frequent pattern mining being a key theme in data mining research. The internet is getting considerably more complicated, which makes it both more crucial and more difficult to process different data mining challenges across diverse areas. This research propose novel technique in frequent pattern data mining based on sequence using clustering and DL architecture. Frequent pattern based data is clustered and classified using fuzzy clustering with back propagation ResNet Convolutional networks. The experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score and RMSE. Proposed technique attained accuracy of 95%, precision of 71%, recall of 65%, F-1 score of 77% and RMSE of 62%.

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References

Cheng, H., Yan, X., Han, J., & Hsu, C. W. (2006, April). Discriminative frequent pattern analysis for effective classification. In 2007 IEEE 23rd international conference on data engineering (pp. 716-725). IEEE.

Hsu, M. J., Chien, Y. H., Wang, W. Y., & Hsu, C. C. (2020). A convolutional fuzzy neural network architecture for object classification with small training database. International Journal of Fuzzy Systems, 22(1), 1-10.

proposed frequent pattern data mining based on sequence

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Published

19.12.2022

How to Cite

Niralgikar, K. H. ., & A. Bulsara, M. . (2022). Frequent Pattern Data Mining Based Clustering and Classification Using Fuzzy Back Propagation Resnet Convolutional Networks. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 201–204. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2384

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

License

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