Competent K-means for Smart and Effective E-commerce

  • Akash GujarathiEmail author
  • Shubham Kawathe
  • Debashish Swain
  • Subham Tyagi
  • Neeta Shirsat
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 668)


The paper compares various clustering algorithms with k-means algorithm used in e-commerce. It gives a brief introduction to the e-commerce system. K-means algorithm is largely used for the clustering, so it investigates the k-means algorithm and factors out the advantages and the drawbacks of the traditional k-means approaches. For the drawbacks of the traditional approaches, the paper tries to refine the traditional algorithm. The new algorithm is expected to increase the effectiveness and the cluster quality. Paper also proposes a unique collaborative recommendation pool approach based on k-means clustering algorithm. We adopt the modified cosine similarity to figure out the similarity between users in the same clusters. Then, we produce recommendation results for the target users. By mathematical analysis, we prove that our clustering algorithm surpasses traditional k-means algorithm.


Data mining Clustering Marketing K-means Cosine similarity Association rules 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Akash Gujarathi
    • 1
    Email author
  • Shubham Kawathe
    • 1
  • Debashish Swain
    • 1
  • Subham Tyagi
    • 1
  • Neeta Shirsat
    • 1
  1. 1.Department of Information TechnologyPVG’s College of Engineering and TechnologyPuneIndia

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