Innovative study on clustering center and distance measurement of K-means algorithm: mapreduce efficient parallel algorithm based on user data of JD mall-Reference-Cited by-同舟云学术

Innovative study on clustering center and distance measurement of K-means algorithm: mapreduce efficient parallel algorithm based on user data of JD mall

Published:2021-03-31 Issue: Volume: Page:
ISSN:1389-5753
Container-title:Electronic Commerce Research
language:en
Short-container-title:Electron Commer Res

Author:

Liu Yang^ORCID,Du Xinxin,Ma Shuaifeng

Publisher

Springer Science and Business Media LLC

Subject

Human-Computer Interaction,Economics, Econometrics and Finance (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s10660-021-09458-z.pdf

Reference60 articles.

1. Chakraborty, S., & Das, S. (2017). k Means clustering with a new divergence-based distance measure: Convergence and performance analysis. Pattern Recognition Letters, 100, 67–73.

2. Ailon, N., Jaiswal, R., & Monteleoni, C. (2009). Streaming K-means approximation. International Conference on Neural Information Processing Systems.

3. Albanese, A., Pal, S. K., & Petrosino, A. (2011). A rough set approach to spatio-temporal outlier detection. Berlin Heidelberg: Springer.

4. Arthur, D., & Vassilvitskii, S. (2007). K-Means++: The advantages of careful seeding. proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, SODA 2007, New Orleans, Louisiana, USA, January 7–9, 2007.

5. Breunig, M. M., Kriegel, H. P., Ng, R., & Sander, J. (2000). LOF: Identifying density-based local outliers. Acm Sigmod Record, 29(2), 93–104.

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