Analysis of Waste Water Treatment plant to enhance the Eco-friendly Environment using Data Mining Techniques

  • Authors

    • Meghana. V
    • Mamatha Bai.B.G
    • Jharna Majum
    • . .
    https://doi.org/10.14419/ijet.v7i3.29.21398
  • Data Mining, Waste Water Treatment Plant, G-means, CLARA, DBSCAN
  • Abstract

    E-commerce is one of the rapidly booming sectors in India today, thanks to the rising internet user base and faster mobile penetration.  The E-commerce industry is a complex ecosystem as it involves huge transaction volumes, complex procurement and logistics systems and reliance on new technologies for customer access and payment transactions. This complexity has given rise to frauds and revenue leakages which is impacting the revenue for the ecommerce companies. Hence the major concern facing the Ecommerce sector today is how to mitigate the revenue loss. Very few studies have been done in academic literature in this area hence the objective of this study is to understand the sources of revenue leakage in the ecommerce sector and propose solutions for mitigating these revenue leakages. The study focuses on 2 major areas of revenue leakage viz. Customer side, Vendor side. The proposed revenue assurance model will be helpful to Ecommerce companies for detecting the sources of revenue leakages in the abovementioned areas and plugging the same thereby reducing losses. The study can also be helpful for consulting companies who are in the business of revenue assurance and fraud management for the ecommerce companies.

     

     

  • References

    1. [1] Deepashri.K.S, AshwiniKamath, “Survey on techniques of data mining and its applicationsâ€, International Journal of Emerging Research in Management &Technology ,February 2017,ISSN 2278-9359 ,Volume-6, Issue-2.

      [2] JiaQiao, Yong Zhang, “Study on K-means Method Based on Data-Miningâ€, IEEE, 2015.

      [3] TomislavErdelić, SilvijaVrbanÄić, LovroRožić, “A Model of Speed Profiles for Urban Road Networks Using G-mea-ns Clusteringâ€, MIPRO 2015, 25-29 May 2015, Opatija, Croatia

      [4] Aislan G. Foina, JuditPlanas, Rosa M. Badia, “P-Means, a Parallel Clustering Algorithm for a Heterogeneous Multi-Processor Environmentâ€, Intelligence Research Institute (IIIA), IEEE, 2011

      [5] Greg Hamerly, Charles Elkan, “Learning the k in k-meansâ€, Department of Computer Science and Engineering, University of California

      [6] Xiao Dong, Zhongnan Zhang, “Research and Implementation of PAM Algorithm with Time Constraintsâ€, International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), 2014

      [7] M. OmairShafiq, Eric Torunski, “A Parallel K-Medoids Algorithm for Clustering based on Map Reduceâ€, International Conference on Machine Learning and Applications, IEEE,2016

      [8] Raymond T. Ng and Jiawei Han, Member, “CLARANS: A Method for Clustering Objects for Spatial Data Miningâ€, IEEE Transactions on knowledge and Data Engineering, September/October 2002, VOL. 14, NO. 5

      [9] S.Vijayarani, S.Nithya, “An Efficient Clustering Algorithm for Outlier Detectionâ€, International Journal of Computer Applications (0975 – 8887), October 2011, Volume 32– No.7

      [10] Glory H.Shah,“An Improved DBSCAN, A Density Based Clustering Algorithm with Parameter Selection for High Dimensional Data Setsâ€,Nirma university international conference on engineering, 06-08december, 2012

      [11] Yuchao Zhang, Hongfu Liu, Bo Deng, “Evolutionary Clustering with DBSCANâ€, 2013 Ninth International Conference on Natural Computation (ICNC)

      https://archive.ics.uci.edu/ml/datasets/water+treatment+plant

  • Downloads

  • How to Cite

    V, M., Bai.B.G, M., Majum, J., & ., . (2018). Analysis of Waste Water Treatment plant to enhance the Eco-friendly Environment using Data Mining Techniques. International Journal of Engineering & Technology, 7(3.29), 655-659. https://doi.org/10.14419/ijet.v7i3.29.21398

    Received date: 2018-10-09

    Accepted date: 2018-10-09