Visual analytics systems combine machine learning or other analytic techniques with
interactive data visualization to promote sensemaking and analytical reasoning. It is through …

Unlike traditional unsupervised clustering, semi-supervised clustering allows users to
provide meaningful structure to the data, which helps the clustering algorithm to match the …

Abstract Machine learning (ML) is the fastest growing field in computer science, and health
informatics is among the greatest challenges. The goal of ML is to develop algorithms which …

In this survey, 105 papers related to interactive clustering were reviewed according to seven
perspectives:(1) on what level is the interaction happening,(2) which interactive operations …

Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …

Hierarchical Clustering (HC) is a widely studied problem in exploratory data analysis,
usually tackled by simple agglomerative procedures like average-linkage, single-linkage or …

$ k $-means clustering is a well-studied problem due to its wide applicability. Unfortunately,
there exist strong theoretical limits on the performance of any algorithm for the $ k $-means …

Hierarchical clustering is a popular unsupervised data analysis method. For many real-world
applications, we would like to exploit prior information about the data that imposes …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Max-cut, clustering, and many other partitioning problems that are of significant importance
to machine learning and other scientific fields are NP-hard, a reality that has motivated …