Motivation How to partition a dataset into a set of distinct clusters is a ubiquitous and
challenging problem. The fact that data vary widely in features such as cluster shape, cluster …

Clustering data has been an important task in data analysis for years as it is now. The de
facto standard algorithm for density-based clustering today is DBSCAN. The main drawback …

Clustering has a wide range of applications in life sciences and over the years has been
used in many areas ranging from the analysis of clinical information, phylogeny, genomics …

Building upon the promising performances of density-based clustering, we present a novel
density-based clustering algorithm called MDCUT (MultiDensity ClUsTering). The presented …

Clustering has a long and rich history in a variety of scientific fields. Finding natural
groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the …

Clustering offers significant insights in data analysis. Density-based algorithms have
emerged as flexible and efficient techniques, able to discover high-quality and potentially …

Clustering is able to find out implicit data distribution and is especially useful in data driven
machine learning. Density based clustering has an attractive property of detecting clusters of …

The density peak based clustering algorithm is a recently proposed clustering approach. It
uses the local density of each data and the distance to the nearest neighbor with higher …

One of the most challenging aspects of clustering is validation, which is the objective and
quantitative assessment of clustering results. A number of different relative validity criteria …

Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …