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Computational Intelligence for Data Analytics

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Recent Advances in Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 823))

Abstract

Computational Intelligence is one of the state of art technology which can be widely used in various applications. Data analytics is one prime area which is implemented in various domains to help the society grow better. The outcome of the analytics helps the decision makers to make better decisions and improve the business. This chapter brings in the implementation of computational intelligence through different machine learning algorithms. Topic modelling is implemented over customer review data set to generate terms and topics to analyse the review and understand the behaviour of the customers towards the product. Various classification algorithms were applied over an educational dataset to analyse the performance of the students and help the tutor to make decisions in changing the course structure. An experimental setup was made to make the algorithms learn the dataset through previous records and then new records were introduced. The model is then evaluated using different metrics and the best model is identified for the selected dataset. This chapter is an application of the above mentioned techniques to perform data analytics in a better way.

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References

  1. Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence A Logical Approach. Copyright © Oxford University Press, New York (1998)

    Google Scholar 

  2. Complete Guide to Topic Modelling. https://nlpforhackers.io/topic-modeling/

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Lear. Res. 3, 993–1022 (2003)

    Google Scholar 

  4. Xu, J.: How to easily do topic modeling with LSA, PLSA, LDA & lda2Vec. Stanford Universtiy. https://medium.com/nanonets/topic-modeling-with-lsa-psla-lda-and-lda2vec

  5. Miran, S.: Latent Dirichlet Allocation (LDA) for Topic Modelling. Project presentation. http://www.ece.umd.edu/smiran/LDA.pdf

  6. Moreno, A., Redondo, T.: Text analytics: the convergence of big data and artificial intelligence. Int. J. Interact. Multimedia Artif. Intell. 3(6), 57–64 (2016)

    Article  Google Scholar 

  7. Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. First published 28 Sept 2017. https://doi.org/10.1002/widm.1230

    Google Scholar 

  8. Sin, K., Muthu, L.: Application of big data in education data mining and learning analytics—a literature review. ICTACT J. Soft Comput. Models Big Data 5(4), 1035–1049 (2015)

    Article  Google Scholar 

  9. Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. syst. Man Cybern. Part C Appl. Rev. 40(6) (2010)

    Article  Google Scholar 

  10. Danso, S.O.: An exploration of classification prediction techniques in data mining: the insurance domain. A Dissertation Presented to the School of Design, Engineering, and Computing. Bournemouth University. http://www.comp.leeds.ac.uk

  11. Anupama Kumar, S., Vijayalakshmi, M.N.: Efficiency of decision trees in predicting student’s academic performance. In: Proceedings of First International conference on Computer science, Engineering and Applications (CCSEA 2011), Chennai, pp. 335–341, 15–17th July 2011, ISSN: 2231-5403

    Google Scholar 

  12. Ukwueze Frederick, N., Okezie Christiana, C.: Evaluation of data mining classification algorithms for predicting students performance in technical trades. Int. J. Eng. Comput. Sci. 5(8), 17593–17601 2016. ISSN: 2319-7242

    Google Scholar 

  13. Anupama Kumar, S., Vijayalakshmi, M.N.: Efficiency of multi instance learning in educational data mining. In: Margret Anouncia, S., Wiil, U. (Eds.) Knowledge Computing and its Applications—Volume II. Springer, Singapore (2018). Print ISBN: 978–981-10-8257-3, Online ISBN: 978-981-10-8258-0

    Chapter  Google Scholar 

  14. Ramesh, V., Thenmozhi, P., Ramar, K.: Study of influencing factors of academic performance of students: a data mining approach. Int. J. Sci. Eng. Res. 3(7) (2012)

    Google Scholar 

  15. Kireyev, K., Palen, L., Anderson, K.: Applications of topics models to analysis of disaster-related twitter data. In: NIPS Workshop on Applications for Topic Models: Text and Beyond, vol. 1 (2009)

    Google Scholar 

  16. Hospedales, T., Gong, S., Xiang, T.: A Markov clustering topic model for mining behaviour in video. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE (2009)

    Google Scholar 

  17. McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems. ACM (2013)

    Google Scholar 

  18. Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora (2010)

    Google Scholar 

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Anupama Kumar, S., Vijayalakshmi, M.N., Divya, T.L., Subramanya, K.N. (2019). Computational Intelligence for Data Analytics. In: Kumar, R., Wiil, U. (eds) Recent Advances in Computational Intelligence. Studies in Computational Intelligence, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-030-12500-4_2

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