Modelling an Adaptive Clustering Model for the Mining Community Using Learning Approaches

Abstract

Data clustering is a crucial phase in data analysis, widely concentrated by data mining communities. Many previous algorithms based on data clustering are related to the endless models that look for sparsity and higher dimensional issues and try to avoid the sequence of information and the data structural data. The recurrent and convolutional neural networks work on deep learning-based models concerning the data as sequences. Yet, the explanation of outcomes and the supervised signals are lacking. The adaptive data clustering model (ADCM) technique is proposed in this system to incorporate the pre-trained data encoders into data clustering tasks. This model depends on the representation of a sequence that breaks the dependencies on the supervision. The proposed system provides experimental outcomes that perform better than the traditional data clustering algorithm and the modern data model, pre-trained on the complete datasets. Additionally, the clustering result explains the significant understanding of the deep learning technique principles. The clustering approach proposes the description model that assists the users in understanding the quality and meaning of the outcome of the clustering process.