K-means Clustering Algorithm Based on E-Commerce Big Data
Indivar Shaik1, Swapna Suhasini Nittela2, Trayabak Hiwarkar3, Srinivas Nalla4
1Indivar Shaik, Research Scholar, Dept. of Computer Science and Engineering, SSSUTMS, Sehore, Madhya Pradesh, India.
2Swapna Suhasini Nittela, Research Scholar, Dept. of Computer Science and Engineering, SSSUTMS, Sehore, Madhya Pradesh, India.
3Dr. Tryambak Hiwarkar, Professor, Dept. of Computer Science and Engineering, SSSUTMS, Sehore, Madhya Pradesh, India.
4Dr. Srinivas Nalla, Principal, Sahaja Institute of Technology & Science for Women, KarimNagar, Telangana, India.
Manuscript received on 20 August 2019. | Revised Manuscript received on 01 September 2019. | Manuscript published on 30 September 2019. | PP: 1910-1914 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21210981119/2019©BEIESP | DOI: 10.35940/ijitee.K2121.0981119
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Abstract: As the technology improving, huge volumes of different types of data is being generated rapidly. Mining such data is a challenging task. One of the important tasks of mining is to group similar objects or similar data into cluster which is very much useful for analysis and prediction. K-means clustering method is a popular partition based approach for clustering data as it leads to good quality of results. This paper focuses on K-means clustering algorithm by analyzing the E-commerce big data. In this research, geographical location and unique identification number of the customer are considered as constraints for clustering.
Keywords: Big data, Clustering, E-commerce, K-means
Scope of the Article: