Clustering and Data Mining

Dear Colleagues,

This Topical Collection on "Clustering and Data Mining" invites submissions on cutting-edge research dedicated to discovering valuable patterns, structures and knowledge from large-scale datasets. This collection covers the theory, algorithms and applications of clustering and data mining, aiming to foster academic exchange among researchers in related fields. We particularly welcome innovative methods that integrate machine learning, statistics and domain knowledge to solve real-world problems.

Topics of interest for this collection include, but are not limited to, the following:

• Data Mining Techniques: Association rule mining, sequential pattern mining, classification, regression and anomaly detection.
• Clustering Algorithms: Novel algorithms and improvements in hierarchical, partitional, density-based, grid-based and model-based clustering.
• High-Dimensional and Complex Data Mining: Text mining, web mining, stream data mining and multimedia data mining.
• Interpretability and Visualization: Methods for understanding, interpreting and visualizing the results of data mining models.
• Application Case Studies: Successful applications of data mining and clustering in areas such as business intelligence, market analysis, biomedicine, financial risk management and social sciences.

Prof. Dr. Feiping Nie
Collection Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.