A Healthcare Application Model for Smarthome Using Frequent Pattern Based KNN Algorithm

  • Amrutha RaveendranEmail author
  • U. Barakkath Nisha
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 44)


Nowadays most of the people are moving from rural to urban areas and they would rather prefer the advanced healthcare applications in their daily life. So in this century, the studies behind this idea are growing fast. In urban areas most of the homes are being equipped with smart devices and therefore the scope of the healthcare applications in this area can be done without any awkwardness. In this paper, the data collected from the smart devices can be used as the source data for this purpose. The human activities are monitored by the proposed system. From this huge data the patterns are being recognized by the use of Frequent Pattern mining and the appliance to appliance and appliance to time associations are built using incremental k-means clustering algorithm. The activity prediction will be done by frequent pattern based KNN algorithm. This system can predict the human activity pattern along with greater accuracy for the purpose of healthcare applications.


Frequent pattern mining Incremental K-means clustering Frequent pattern based KNN algorithm 



All Author states that there is no conflcit of interest. We used UK dale dataset ( in this study.


  1. 1.
    Yassin, A., Singh, S., Alamri, A.: Mining human activity pattern from smart home bigdata for health care applications. IEEE Access 5, 13131–13141 (2007). ISSN: 2169-3536Google Scholar
  2. 2.
    Gajowniczek, K., Ząbkowski, T.: Data mining techniques for detecting household characteristics based on smart meter data. Energies 8, 7407–7427 (2015)Google Scholar
  3. 3.
    Karpagam, V., Pooja, M.U., Swathi, M.: Mining human activity patterns from smarthome big data by using MapReduce algorithm. Glob. Res. Dev. J. Eng. (2018). ISSN: 2455-5703Google Scholar
  4. 4.
    Nandhini, R., Viswanadhan, B.: A health care service model using human activity patterns. In: International Conference on Applied Soft Computing Techniques (2018). ISSN: 2455-1341Google Scholar
  5. 5.
    Liao, J., Stankovic, L., Stankovic, V.: Detecting household activity patterns from smart meter data. In: International Conference on Intelligent Environments, Department of Electronic and Electrical Engineering University of Strathclyde, Glasgow, UK (2014). ISBN: 978-1-4799-2947-4Google Scholar
  6. 6.
    Yadav, A., Singh, G.: Incremental k-means Clustering Algorithms: A Review. Int. J. Latest Trends Eng. Technol. 5Google Scholar
  7. 7.
    Zhang, M., He, C.: Survey on association rules mining algorithms. In: Advancing Computing, Communication, Control and Management, pp. 111–118. LNEE 56 (2010)Google Scholar
  8. 8.
    Alghamdi, A.S.A.: Efficient implementation of FP growth algorithm-data mining on medical data. Int. J. Comput. Sci. Netw. Secur. 11(12) (2011)Google Scholar
  9. 9.
    Ahalya, G., Pandey, H.M.: Data clustering approaches survey and analysis. In: 1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management (2015)Google Scholar
  10. 10.
    Jadhav, J., Ragha, L., Katkar, V.: Incremental frequent pattern mining. Int. J. Eng. Adv. Technol. (IJEAT) 1(6) (2012). ISSN: 2249 – 8958Google Scholar
  11. 11.
    Gu, J., Zhou, J., Chen, X: An enhancement of k-means clustering algorithm. In: International Conference on Business Intelligence and Financial Engineering, Aug 2009Google Scholar
  12. 12.
    Shah, N.S.: A modified approach for incremental k-means clustering algorithm. IJEDR 3, 1081–1084 (2015). ISSN: 2321-9939Google Scholar
  13. 13.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7) (2002)Google Scholar
  14. 14.
    Wang, L., Fan, X.J., Liu, X.L., Zhao, H.: Mining data association based on a revised fp-growth algorithm. In: International conference on machine learning and cybernetics, Nov 2012Google Scholar
  15. 15.
    Song, Y., Wei, R.: Research on application of data mining based on fp-growth algorithm for digital library. IEEE (2011)Google Scholar
  16. 16.
    Qi, J., Yu, Y., Wang, L., Liu, J.: K*-Means: an effective and efficient k-means clustering algorithm. In: IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom), Oct 2016Google Scholar
  17. 17.
    Taneja, S., Gupta, C., Goyal, K., Gureja, D.: An enhanced k-nearest neighbor algorithm using information gain and clustering. In: Fourth International Conference on Advanced Computing & Communication Technologies, Apr 2014. ISBN: 978-1-4799-4910-6Google Scholar
  18. 18.
    Sun, S., Huang, R.: An adaptive k-nearest neighbor algorithm. In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Sept 2010Google Scholar
  19. 19.
    Jivani, A.G.: The novel k nearest neighbor algorithm. In: International Conference on Computer Communication and Informatics, February 2013Google Scholar
  20. 20.
    Gajowniczek, K., Za_bkowski, T.: Data mining techniques for detecting household characteristics based on smart meter data. Energies 8, 7407 (2015)Google Scholar
  21. 21.
    Zhou, Cheng, Cule, Boris, Goethals, Bart: Pattern based sequence classification. IEEE Trans. Knowl. Data Eng. 28, 1285–1298 (2015)CrossRefGoogle Scholar
  22. 22.
    Hassanat, A.B., Abbadi, M.A.: Solving the problem of the k parameter in the KNN classifier using an ensemble learning approach. Int. J. Comput. Sci. Inf. Secur. 12(8) (2014)Google Scholar
  23. 23.
    Peddi, S.V.B., Kuhad, P., Yassine, A., Pouladzadeh, P., Shirmohammadi, S., Shirehjini, A.A.N.: An intelligent cloud-based data processing broker for mobile e-health multimedia applications. Future Generat. Comput. Syst. J. 66, 71–86 (2017)Google Scholar
  24. 24.
    Hossain, M.S.: A patient’s state recognition system for health care using speech and facial expression. J. Med. Syst. 40(12), 272:1–272:8 (2016)Google Scholar
  25. 25.
    Ul Alam, M., Roy, N., Petruska, M., Zemp, A.: Smart-energy group anomaly based behavioral abnormality detection. In: Proceedings of the IEEE Wireless Health, Oct 2016, pp. 1–8 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSE, ICETKTU UniversityKeralaIndia

Personalised recommendations