Abstract
Examination, plan, and usage of programming frameworks for online administrations are a repetitive and testing. Amazon programming gives item proposals, Yahoo! powerfully suggests web pages, afflux makes proposals for films, and Google makes ads on the Internet. Things are suggested in view of the inclinations, needs, attributes, and conditions of clients. The wine informational index has been used in examine for quite a while, and still, it stays as the benchmark informational collection. Nature of wines is hard to characterize as there are numerous variables that impact the apparent quality. This paper exhibits a basic survey of research slants on wine quality and client-driven closeness measures also. A novel client-driven likeness measure in item bunching is proposed to assess the prominent wine informational index named red wine dataset. The test results got in this work can give preferable proposals to item purchasers over the current frameworks. The proposed approach is skilled to gather the red wine dataset into requested gatherings of favored wine variations and can judge the wine quality in view of these client inclination gatherings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hu, Gongzhu, Tan Xi, and Faraz Mohammed. 2013. Classification of Wine Quality with Imbalanced Data. March 2016 IEEE. https://doi.org/10.1109/icit.2016.7475021.
Haydar, Charif, Anne Boyer. 2017. A New Statistical Density Clustering Algorithm based on Mutual Vote and Subjective Logic Applied to Recommender Systems. UMAP’17, July 9–12, 2017, Bratislava, Slovakia.
Song, Linqi, Cem Tekin, Mihaela van der Schaar. 2014. Clustering Based Online Learning in Recommender Systems: A Bandit Approach, May 2014, ICASSP2014. https://doi.org/10.1109/icassp.2014.6854459.
Chen, Bernard, Christopher Rhodes, and Aaron Crawford. 2014. Wine Informatics: Applying Data Mining on Wine Sensory Reviews Processed by the Computational Wine Wheel. In 2014 IEEE International Conference on Data Mining Workshop. https://doi.org/10.1109/icdmw.2014.149.
Parvin, Hamid, Hoseinali Alizadeh, and Behrouz Minati. 2010. A Modification on K-Nearest Neighbor Classifier. GJCST 10 (14) (Ver.1.0) November 2010.
Nagarnaik, Paritosh, and A. Thomas. 2015. Survey on Recommendation System Methods. In IEEE Sponsored 2nd International Conference on Electronics and Communication System (ICECS), Feb 27, 2015. https://doi.org/10.1109/ecs.2015.7124857.
Kavuri, N.C., and Madhusree Kundu. 2011. ART1 Network: Application in Wine Classification. International Journal of Chemical Engineering and Applications 2 (3).
Thakkar, Kunal, et al. 2016. AHP and Machine Learning Techniques for Wine Recommendation. International Journal of Computer Science and Information Technologies (IJCSIT) 7 (5): 2349–2352.
Latorre, Maria J., Carmen Garcia-Jares, Bernard Medina, and Carlos Herrero. 1994. Pattern Recognition Analysis Applied to Classification of Wines from Galicia (Northwestern Spain) with the Certified Brand of Origin. Journal of Agricultural and Food Chemistry 42 (7): 1451–1455.
Cortez, Paulo, Antonio Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. Modeling Wine Preferences by Data Mining from Physicochemical Properties. https://doi.org/10.1016/j.dss.2009.05.016.
Panda, Mohit Ranjan, Shubham Dutta, and Saroj Pradhan. 2017. Hybridizing Invasive Weed Optimization with Firefly Algorithm for Multi-Robot Motion Planning. Arabian Journal for Science and Engineering.
Lahari, K., M. Ramakrishna Murty. 2015. Partition Based Clustering Using Genetic Algorithms and Teaching Learning Based Optimization: Performance Analysis. In International Conference and Published the Proceedings in AISC, vol. 2, 191–200. Berlin: Springer. https://doi.org/10.1007/978-3-319-13731-5_22.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhagyalaxmi, D., Manjula, R., Ramanababu, V. (2020). A New Framework Approach to Predict the Wine Dataset Using Cluster Algorithm. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)