A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering

Pantula, Priyanka D. and Miriyala, Srinivas S. and Mitra, Kishalay (2021) A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering. In: 7th Indian Control Conference, ICC 2021, 20 December 2021 through 22 December 2021, Virtual, Online.

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Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of data science. Since the task of automated feature extraction is proficiently combined with the machine learning models in deep unsupervised learning algorithms, they are identified to be superior as compared to conventional dynamic similarity measure based clustering methods. In this context, the authors present a recurrent neural network (RNN) based clustering algorithm optimization, where the vital information representing the dynamic data (or time-series data) is extracted first and subsequently clustered using a soft clustering algorithm. This methodology not only ensures dynamic component extraction in terms of static features but also clusters them efficiently using an evolutionary clustering algorithm called Neuro-Fuzzy C-Means (NFCM) clustering, which reduces the large-scale optimization problem of FCM to small-scale along-with identification of optimal number of clusters. The proposed algorithm has been implemented on three different test data sets collected from machine learning repository and it was found that the results are 98-100% accurate. © 2021 IEEE.

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IITH Creators:
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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Deep unsupervised learning; Feature extraction and Optimal cluster number; Neuro-Fuzzy C-means clustering; Recurrent Neural Networks; Time-series data
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 03 Aug 2022 09:16
Last Modified: 03 Aug 2022 09:16
URI: http://raiith.iith.ac.in/id/eprint/10071
Publisher URL: http://doi.org/10.1109/ICC54714.2021.9703152
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