Lasso-Based k-Means++ Clustering

Clustering is a powerful and efficient technique for pattern recognition which improves classification accuracy. In machine learning, it is a useful unsupervised learning approach due to its simplicity and efficiency for clustering applications. The curse of dimensionality poses a significant challenge as the volume of data increases with rapid technological advancement. It makes traditional methods of analysis inefficient. Sparse clustering is essential for efficiently processing and analyzing large-scale, high-dimensional data. They are designed to handle and process sparse data efficiently since most elements are zero or lack information. In data science and engineering applications, they play a vital role in taking advantage of the natural sparsity in data to save computational resources and time. Motivated by recent sparse k-means and k-means++ algorithms, we propose two novel Lasso-based k-means++ (Lasso-KM++) clustering algorithms, Lasso-KM1++ and Lasso-KM2++, which incorporate Lasso regularization to enhance feature selection and clustering accuracy. Both Lasso-KM++ algorithms can shrink the irrelevant features towards zero, and select relevant features effectively by exploring better clustering structures for datasets. We use numerous synthetic and real datasets to compare the proposed Lasso-KM++ with k-means, k-means++ and sparse k-means algorithms based on the six performance measures of accuracy rate, Rand index, normalized mutual information, Jaccard index, Fowlkes–Mallows index, and running time. The results and comparisons show that the proposed Lasso-KM++ clustering algorithms actually improve both the speed and the accuracy. They demonstrate that our proposed Lasso-KM++ algorithms, especially for Lasso-KM2++, outperform existing methods in terms of efficiency and clustering accuracy.