Data Points Clustering via Gumbel Softmax

Abstract

Finding useful patterns in the dataset has been a fascinating topic, and one of the most researched problems in this area is identifying the cluster groups within the dataset. This research paper introduces a "new data clustering method" called Data Points Clustering via Gumbel Softmax (DPCGS) and demonstrates that it is suitable for clustering the data points datasets. We evaluate DPCGS efficiency and clustering quality through several experiments. Experiments show that statistically relevant clustering structures can be identified with our method, depending on the dataset. We also present a performance comparison table where we use datasets such as Wine, Wheat seeds, Iris, Wisconsin breast cancer and compare the DPCGS results with different benchmarking and recently proposed clustering algorithms such as Birch, K-Means, Affinity propagation, Agglomerative clustering, and Mini-batch K-Means and Nested mini-batch K-Means. Our method DPCGS performs better than most of the previously and recently proposed clustering algorithms.

Dual-Branch-AttentionNet: A Novel Deep-Learning-Based Spatial-Spectral Attention Methodology for Hyperspectral Data Analysis

“…Over the past few years, deep learning based HSI classification frameworks with stratified feature learning ability have dominated the domain of computer vision, and have produced superlative results in various remote sensing applications [21][22][23]. Not only do these deep learning-based models have the capability to learn more convoluted and abstract features in the data [24,25], they also hold the intrinsic capability of learning higher level features present in the shallow part of the network. Such models are typically not affected by the changes to the input, and have produced unprecedented results in spectral-spatial feature extraction-based HSI classification frameworks [6,8].…”