Dr Ankit Mune

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Computer Science Applications

Scopus Publications

TUMKFCM-ELM: An Unsupervised Multiple Kernelized Fuzzy C-Means Extreme Learning Machine Approach for Heterogeneous Datasets
Mune Ankit R. and Bhura Sohel A.
Totem Publisher, Inc.



Three-Stage Heterogeneous Data Clustering Using Unsupervised Multiple Kernel and Extreme Learning Machine
Ankit R. Mune and Sohel A. Bhura
Springer Nature Singapore



An analysis of heterogeneous data with extreme learning via unsupervised multiple kernels
Ankit R. Mune and Sohel A. Bhura
IEEE
Multiple sources without information about labels collect a large amount of data, often containing heterogeneous data, namely various types, structures as well as distributions. Such data can include Instagram, Twitter, and Facebook and YouTube, texts, images and videos. Advanced unsupervised learning methods (with multiple kernels) cabs are applied to extract information from such great unlabeled heterogeneous data MKL Traditional MKL algorithms are a good way to reveal information from multiple sources. Some efforts in managing difficult, heterogeneous distributed data, for example using the kernel, have currently been made to efficiently capture data from heterogeneous data. In recent times some unsupervised learning efforts have been made. In this paper, we illustrate the problem by heterogeneous knowledge and discuss various kernel and extreme learning approaches and their problems without supervision.

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