A study on sequential pattern mining on chemical information

  • Authors

    • S Sathya
    • N Rajendran
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14828
  • Data Mining (DM), Chemical Compounds, Chemical Bonding, Sequential Pattern Mining

  • Abstract

    Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields. Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation. Chemical bonding is the association of atoms into molecules, ions, crystals and other stable species which frame the common substances in chemical information. However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications. In this work, chemical structured index bonding is used for sequential pattern mining. Our research work helps to evaluate the structural patterns of chemical bonding in chemical information data sets.

     

     

  • References

    1. [1] HyunJi Kim, Byong Su Choi and Moon Yul Huh, “Booster in high dimensional data classificationâ€, IEEE Transactions on Knowledge and Data Engineering, 2016, Volume 28, Issue 1, Pages 29-40.

      [2] Chuanren Liu, Kai Zhang, Hui Xiong, Geoff Jiang and Qiang Yang, “Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualizationâ€, IEEE Transactions on Knowledge and Data Engineering, Year 2016, Volume 28, Issue 1, Pages 211-223.

      [3] Massimiliano Albanese, Cristian Molinaro, Fabio Persia, Antonio Picariello, and V.S. Subrahmanian, “Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Dataâ€, IEEE Transactions on Knowledge and Data Engineering, March 2014, Volume 26, Issue 3, Pages 577-594. [4] Minyoung Kim, “Probabilistic Sequence Translation-Alignment Model for Time-Series Classificationâ€, IEEE Transactions on Knowledge and Data Engineering, February 2014, Volume 26, Issue 2, Pages426-437. [5] Shuo Wang, Leandro L. Minku, and Xin Yao, “Resampling-Based Ensemble Methods for Online Class Imbalance Learningâ€, IEEE Transactions on Knowledge and Data Engineering, May 2015, Volume 27, Issue 5, Pages 1356-1368.

      [4] Buyue Qian, Xiang Wang, Jieping Ye, and Ian Davidson, “A Reconstruction Error Based Framework for Multi-Label and Multi-View Learningâ€, IEEE Transactions on Knowledge and Data Engineering, March 2015, Volume 27, Issue 3, Pages 594-607.

      [5] Dominik Fisch, Edgar Kalkowski, and Bernhard Sick, “Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applicationsâ€, IEEE Transactions on Knowledge and Data Engineering, March 2014, Volume 26, Issue.3, Pages 652-666.

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  • How to Cite

    Sathya, S., & Rajendran, N. (2018). A study on sequential pattern mining on chemical information. International Journal of Engineering & Technology, 7(2.33), 532-535. https://doi.org/10.14419/ijet.v7i2.33.14828

    Received date: 2018-06-30

    Accepted date: 2018-06-30

    Published date: 2018-06-08