Adaptive Recovery of Distorted Data Based on Credibilistic Fuzzy Clustering Approach

Yevgeniy V. Bodyanskiy, Alina Shafronenko, Iryna Klymova
2021 International Conference on Computational Linguistics and Intelligent Systems  
The problems of big data clustering is very interesting area of artificial intelligence nowadays. This task often occurs in many application, that related with data mining, deep learning, web mining etc. For solving these problems the traditional approaches and methods require that every vectorobservation from processed data set is fed in batch form and does not change over the time and could belong more, than one cluster. In this situation more effective are fuzzy clustering methods that are
more » ... nthesized under the assumptions of mutual overlapping of classes, based on credibility theory and adaptive goal function. Therefore as alternative, to known clustering algorithms we propose adaptive recovery of distorted data algorithm based on credibilistic fuzzy clustering approach.
dblp:conf/colins/BodyanskiySK21 fatcat:tco2w3i5j5eahhv4lewkjmseza