Abstract
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.
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Gujarathi, A., Kawathe, S., Swain, D., Tyagi, S., Shirsat, N. (2018). Competent K-means for Smart and Effective E-commerce. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_23
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DOI: https://doi.org/10.1007/978-981-10-7868-2_23
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