Data Clustering: Algorithms and Applications

Dear Colleagues,

At present, with the rapid growth of computer science and information technology, the amount of data obtained has increased significantly. Such pools of data are ubiquitous and play important roles in many fields of business and science. Using existing data analysis methods to reveal natural structures and identify interesting patterns in the underlying data, as well as interpreting the results, represents a vast challenge. Clustering has become a fundamental and commonly used technique for knowledge discovery and data mining. Still, the need to cluster huge datasets with a high dimensionality poses a challenge to clustering algorithms. The collecting and use of data for analysis purposes needs to be fast in real applications. However, a large proportion of data have irrelevant features that may cause a decrease in processing efficiency. These necessitate higher requirements for the effectiveness of clustering methods. Hence, it is important to treat cluster analysis, anomaly detection, and dimensionality reduction as concepts that are not inseparable.

This Special Issue focuses on data clustering as well as knowledge discovery and machine learning. The aim of this Special Issue is to compile the recent advances in this contemporary research area, studies of the primary aspects of data clustering, key techniques commonly used for clustering, and insights discussing important features of the clustering process in a variety of application areas. We invite high-quality submissions from researchers of the field of data clustering to exchange and share their experiences and research results, whether theoretical or applicational.

Prof. Dr. Małgorzata Charytanowicz
Prof. Dr. Piotr A. Kowalski
Guest Editors

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