Data mining is an analytical approach that contributes to achieving a solution to many
problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable …

We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly
identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering …

Extant research on hotel open innovation rarely explores innovative ideas from customer-
generated online reviews and pays little attention to consumers' affective needs. To bridge …

Clustering is a fundamental tool aiming at classifying data points into groups based on their
pairwise distances or similarities. It has found successful applications in all natural and …

Over the last decade, the growth of the global pharmaceutical market has led to an overall
increase of substandard and falsified drugs especially on the African market (or emerging …

Appropriately handling the scalability of clustering is a long-standing challenge for the study
of clustering techniques and is of fundamental interest to researchers in the community of …

The article presents immediate access to over fifty fundamental clustering algorithms.
Additionally, access to clustering benchmark datasets published priorly as “Fundamental …

Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin
indices) are frequently used for selecting the appropriate number of partitions a dataset …

A massive amount of textual data now exists in digital repositories in the form of research
articles, news articles, reviews, Wikipedia articles, and books, etc. Text clustering is a …

Inferring the structure of human populations from genetic variation data is a key task in
population and medical genomic studies. Although a number of methods for population …