Abstract
The paper discusses an algorithm that groups the items on the basis of their attributes and then classifies the clusters. In other words, the proposed algorithms first cluster the items on the basis of property, i.e., attributes available for the dataset. The clustering is performed by K-means clustering. Then this clustered data is classified using the RepTree. In other words, the proposed algorithm is the hybrid algorithm of K-means clustering and the RepTree classification. The proposed algorithm is compared with the RepTree algorithm using the WEKA tool. The comparison is done over clothing dataset downloaded from Internet. The proposed algorithm decreases the mean absolute error as well as the root-mean-square error. The decrease in error results in accurate classification. So the proposed algorithm clusters the items and classifies them on the basis of their attributes more accurately.
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Midha, N., Singh, V. (2018). Classification of E-commerce Products Using RepTree and K-means Hybrid Approach. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-10-6620-7_26
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DOI: https://doi.org/10.1007/978-981-10-6620-7_26
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