ABSTRACT
Mobile Internet has gradually penetrated into all aspects of the daily life. Ever explosive growth recently hit the New Retail, which is closely integrated Internet online advantages with the offline stores-based facilities. Users can choose the most convenient stores for online or offline consumption, which determines that there are common users among stores, and the sales of stores could interact with each other. To make stores' operation network more efficient, the relationships among stores are explored and most efficient store clusters are identified, considering the geographical positions and business dependencies of different stores. In this paper, we first build business correlation matrix based on common user among stores respectively. Second, a constrained spectral clustering model is established to correct the outliers in each unsupervised iteration. Finally, the business data of Luckin Coffee are collected to validate our model. The results show that our method outperforms pure K-means and pure Spectral Clustering, which achieves an appropriate balance between spatial aggregation and business aggregation. This method can be applied to other new retail scenarios where stores have businesses interaction with each other.
- Donath W E, Hoffman A J. Lower bounds for the partitioning of graphs. IBMJ. Res. Develop. 1973(17), 420--425. Google ScholarDigital Library
- Fiedler M. Algebraic connectivity of graphs. Czech, Math. J, 1973(23), 298--305.Google Scholar
- Dhillon, I.Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, New York: ACM Press, 2001, PP. 69--274. Google ScholarDigital Library
- Dhillon, I., Guan, Y., and Kulis, B. A untied view of kernel k- means, spectral clustering, and graph partitioning. University of Texas at Austin, 2005.Google Scholar
- Bach, F. and Jordan, M. Learning spectral clustering. In S. Thrun L. Saul, and B. SchSlkopf (Eds), Advances in Neural Information Processing Systems. Cambridge, MA:MIT Press, 2004. Google ScholarDigital Library
- Kempe, D. and McSherry, F. A decentralized algorithm for spectral analysis. In Proceedings of the 36th Annual ACM Symposium on Theory of Computing. New York, NY, USA: ACM Press, 2004, PP. 561--5 68. Google ScholarDigital Library
- Perez A, Andres C, and Johan S. Sparse Kernel spectral clustering models for large -scale data analysis, Neurocomputing, 2011, v74(9 ), p1382--1390. Google ScholarDigital Library
- Jia J, Xiao X, Liu B and Jiao L, Bagging-based spectral clustering ensem ble selection, Pattern Recognition Letter, v32(10), 2011. Google ScholarDigital Library
- Zhang Z and Jordan M. I., Muhiway Spectral Clustering: A Margin-Based Perspective, V 23(3), 2008, p383--403.Google Scholar
- Perona P, Freeman W T. A factorization approach to grouping, Proc. ECCV, 1998, 655--670. Google ScholarDigital Library
- Ahn I, Kim C. Face and Hair Region Labeling Using Semi---Supervised Spectral Clustering Based Multiple Segmentations{J}. IEEE Transactions on Multimedia, 2016, 1--1. DOI= http://dx.doi.org/7448944. Google ScholarDigital Library
- Wang D, Gu J. Integrative clustering methods of multi---omics data for molecule---based cancer classifications{J}. Quantitative Biology. 2016, 1--10.Google Scholar
- Kannan R, Vempala S, Vetta A. On clusterings good, bad and spectral. In FOCS, 2000, 367--377. Google ScholarDigital Library
- Mall R, Bensmail H, Langone R, et al. Denoised Kernel Spectral Data Clustering{C}// International Joint Conference on Neural Networks. IEEE, 2016.Google Scholar
- Son J W, Jeon J, Lee S Y, et al. Adaptive spectral co-clustering for multiview data{C}// International Conference on Advanced Communication Technology. 2016.Google Scholar
- Menéndez, Héctor D, Camacho D. GANY: A genetic spectral-based Clustering algorithm for Large Data Analysis.{C}// Evolutionary Computation. IEEE, 2015.Google Scholar
- Li Y, Guo C. Hypergraph-based spectral clustering for categorical data{C}// Seventh International Conference on Advanced Computational Intelligence. IEEE, 2015.Google Scholar
- Minkowski, Hermann (1910), Geometrie der Zahlen, Leipzig and Berlin: R. G. Teubner, JFM 41.0239.03, MR 0249269, retrieved 2016-02-28.Google Scholar
Index Terms
- Recognition of Stores' Relationship Based on Constrained Spectral Clustering
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