Using the Deep Constrained Clustering Approach to Create a Business Profile

Authors

  • Abdul Latif Universitas Sumatera Utara
  • Sutarman universitas sumatera utara
  • Open Damius universitas sumatera utara

DOI:

10.33395/sinkron.v7i3.11594

Abstract

Identification of customers in the business sector that really needs to be done as an evaluation of a business that is run so that it can continue to grow and be able to follow business developments in the same sector. The deep constraint clustering approach is used to cluster customers towards a business. In this study, a clustering of customers using rail mass transportation will be carried out. The results achieved are the formation of 6 clusters using trains be built. The result of research expected to be a consideration in improving services to the company

GS Cited Analysis

Downloads

Download data is not yet available.

References

Armando Ortuno Padillaa, Jairo Casares Blanco, Shopping centre clusters: Competition or synergies? The case of the region of murcia (Spain). Journal of Retailing and Consumer Services 52 (2020) 101867

Emma M. Sánchez, Julio B. Clempner, Alexander S. Poznyak, Solving the mean–variance customer portfolio in Markov chains using iterated quadratic/Lagrange programming: A credit-card customer limits approach. Expert Systems with Applications xxx (2015) xxx–xxx

N. Ferracutia, C. Norscini, E. Frontoni, P. Gabellini, M. Paolanti, V. Placidi, A business application of RTLS technology in Intelligent Retail Environment: Defining the shopper’s preferred path and its segmentation. Journal of Retailing and Consumer Services 47 (2019) 184–194

Pedro M. Santos, Leonid Kholkine, Andre Cardote, Ana Aguiar, Context classifier for position-based user association control in vehicular hotspots. Computer Communications xxx (xxxx) xxx–xxx

Rozita, A.L., Nor Zana , A.A., Khairulzaman, H., Norlizah, A.H, Impact of Sport Complex Services towards Costumer Behaviour in Terengganu. Procedia - Social and Behavioral Sciences 153 ( 2014 ) 410 – 418

Sandra Jardi, Carlos Mora, Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning. Procedia Computer Science 196 (2022) 199–206

Sanjiv Kumara, Alexander C. Louib, Martial Heberta, An observation-constrained generative approach for probabilistic classification of image regions. Image and Vision Computing 21 (2003) 87–97

Sridhar Raj S, Munaga V.N.K. Prasad, Ramadoss Balakrishnan, Spatio-Temporal association rule based deep annotation-free clustering(STARDAC) for unsupervised person re-identification. Pattern Recognition 122 (2022) 108287

Xiaogang Hou, Haiying Zhao, Yan Ma, Wei Zhou, Adaptive segmentation of traditional cultural pattern based on superpixel Log-Euclidean Gaussian metric. Applied Soft Computing Journal (2020), doi: https://doi.org/10.1016/j.asoc.2020.106828.

Downloads


Crossmark Updates

How to Cite

Latif, A., Sutarman, & Damius, O. (2022). Using the Deep Constrained Clustering Approach to Create a Business Profile. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(3), 2045-2051. https://doi.org/10.33395/sinkron.v7i3.11594