A popular method for selecting the number of clusters is based on stability arguments: one
chooses the number of clusters such that the corresponding clustering results are" most …

Learning systems, whether traditional or computerized ones, often have a teacher-based
design and use a “one-size-fits-all” approach. This approach ignores learners' differences …

The combined effect of pollution source discharge and sediment adsorption leads to the
rapid enrichment of heavy metals and other pollutants in lake sediments, which poses a …

Learning problems form an important category of computational tasks that generalizes many
of the computations researchers apply to large real-life data sets. We ask, What concept …

Topic modeling refers to the task of discovering the underlying thematic structure in a text
corpus, where the output is commonly presented as a report of the top terms appearing in …

A variety of methods have been developed to identify brain networks with spontaneous,
coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose …

Data analysts in industry and academia make heavy use of market segmentation analysis to
develop tourism knowledge and select commercially attractive target segments. Within …

The k-means algorithm is widely used for clustering, compressing, and summarizing vector
data. In this paper, we propose a new acceleration for exact k-means that gives the same …

We design replicable algorithms in the context of statistical clustering under the recently
introduced notion of replicability from Impagliazzo et al.[2022]. According to this definition, a …

Clustering under most popular objective functions is NP-hard, even to approximate well, and
so unlikely to be efficiently solvable in the worst case. Recently, Bilu and Linial (2010)[11] …