In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Density peaks clustering (DPC) model focuses on searching density peaks and clustering
data with arbitrary shapes for machine learning. However, it is difficult for DPC to select a cut …

In today's age of big data and information, large-scale group decision-making has become
an essential aspect of modern economy, science, and technology. This paper proposes a …

The performance of a partitional clustering algorithm is influenced by the initial random
choice of cluster centers. Different runs of the clustering algorithm on the same data set often …

The hidden Markov model (HMM) is a broadly applied generative model for representing
time-series data, and clustering HMMs attract increased interest from machine learning …

Dividing abstract object sets into multiple groups, called clustering, is essential for effective
data mining. Clustering can find innate but unknown real‐world knowledge that is …

Most text clustering algorithms adopt a single criterion optimization approach, which often
fails to find good clustering solutions for a wide diversity of datasets with different clustering …

Density peaks clustering (DPC) is a succinct and efficient algorithm to discover the structure
of datasets, and it has been used in a number of domains. However, applying DPC to real …

We consider the binary classification problem of static and dynamic mixed data in this paper.
Different from mixed categorical and numerical data, the dynamic variables in the new type …

Context The reuse of software design has been an important issue of software reuse. UML
class diagrams are widely applied in software design and has become DE factor standard …