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Meticulous fuzzy convolution C means for optimized big data analytics: adaptation towards deep learning

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Abstract

In this new era, any business, industrial production, etc. are in need of information in analytics to start and continue its new move towards increasing their outcomes, efficiency, and performance. In this way, many analytics and analytics software’s are making promising results and trying to make more efficient solutions for the betterment of tomorrow. Many basic algorithms like K-means family, FCM family, etc. are used for the process. Nevertheless, processing the insignificant data, which is no way useful and may sometimes distracts the significant features that are most needed, a Deep Learning approach is used before Big-Data analytics. On the other hand, the features of the significant data should have more in-depth understanding to explore more possibilities that could help the better tomorrow. Here we propose a Meticulous Fuzzy Convolution C-Means (MFCCM) algorithm by mutating the nature of Convolutional Neural Network (CNN) to adopt the nature of significant feature understanding of deep learning method. The main novel idea behind this algorithm is to process the data through the optimized Big-Data algorithm through the process of effective feature selection. Here the process involves the enhancement of Deep Learning algorithm (CNN) with the FCM to select the significant features. This algorithm shows promising results as it gives better segmentation even in the presence of variance noisy data.

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Authors and Affiliations

  1. Karpagam College of Engineering, Coimbatore, India

    Nagaraj Balakrishnan, Arunkumar Rajendran & Karthigaikumar Palanivel

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  1. Nagaraj Balakrishnan
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  2. Arunkumar Rajendran
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  3. Karthigaikumar Palanivel
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Correspondence to Nagaraj Balakrishnan.

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Balakrishnan, N., Rajendran, A. & Palanivel, K. Meticulous fuzzy convolution C means for optimized big data analytics: adaptation towards deep learning. Int. J. Mach. Learn. & Cyber. 10, 3575–3586 (2019). https://doi.org/10.1007/s13042-019-00945-2

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