Despite its well-known shortcomings, k-means remains one of the most widely used
approaches to data clustering. Current research continues to tackle its flaws while …

The selection of a proper distance function is crucial for analyzing the data efficiently. To find
an appropriate distance for clustering algorithm is an unsolved problem as of now. The …

Clusters in real data are often restricted to low-dimensional subspaces rather than the entire
feature space. Recent approaches to circumvent this difficulty are often computationally …

Clustering with Bregman divergence has been used in literature to unify centroid‐based
parametric clustering approaches and to allow the detection of nonspherical clusters within …

Kernel $ k $-means clustering is a powerful tool for unsupervised learning of non-linearly
separable data. Since the earliest attempts, researchers have noted that such algorithms …

Bi-clustering refers to the task of partitioning the rows and columns of a data matrix
simultaneously. Although empirically useful, the theoretical aspects of bi-clustering …

Despite its well-known shortcomings, $ k $-means remains one of the most widely used
approaches to data clustering. Current research continues to tackle its flaws while …

The number of bike-sharing services has rapidly increased in many cities worldwide. Bike-
sharing schemes have become a popular and environmentally friendly transportation mode …

We propose the Lasso Weighted $ k $-means ($ LW $-$ k $-means) algorithm as a simple
yet efficient sparse clustering procedure for high-dimensional data where the number of …

The problem of linear predictions has been extensively studied for the past century under
pretty generalized frameworks. Recent advances in the robust statistics literature allow us to …