Dear Colleagues,

We are pleased to invite you to contribute a chapter to our upcoming edited volume, Center-Based Clustering: Theory, Algorithms, and Applications, to be published by Springer Nature, the largest global scientific, technical, and medical ebook publisher. The volume will be published under the Unsupervised and Semi-Supervised Learning series (https://tinyurl.com/4mfur7zr). It will be available in both print and ebook format by late 2026 on SpringerLink, a leading science portal that includes more than 8.4 million documents, an ebook collection with more than 275,000 titles, journal archives digitized back to the first issues in the 1840s, and over 30,000 conference proceedings.

Synopsis of the Volume
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Clustering, the unsupervised classification of patterns into groups, is one of the most critical tasks in exploratory data analysis. The primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history in various scientific disciplines, including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these, center-based algorithms (e.g., batch k-means, online k-means, k-harmonic means, spherical k-means, and fuzzy c-means) are especially popular in modern scientific and engineering applications due to their interpretability, computational efficiency, and optimization-based formulation.

The goal of this volume is to summarize the state of the art in center-based clustering.

Topics of Interest
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Topics of interest include:

Hard clustering algorithms
Fuzzy, probabilistic, or possibilistic clustering algorithms
Information-theoretic clustering algorithms
Neural clustering algorithms
Metaheuristic clustering algorithms
Constrained clustering algorithms
Approximate clustering algorithms
Parallel or distributed clustering algorithms
Dissimilarity or similarity functions for clustering
Objective functions for clustering
Initialization of clustering algorithms
Convergence properties of clustering algorithms
Robustness of clustering algorithms
Algorithms for clustering high-dimensional data
Evaluation of clustering algorithms
Visualization of clustering algorithms
Software or hardware implementations of clustering algorithms
Applications of clustering algorithms

Important dates
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Submission of abstracts: March 1, 2026
Notification of initial editorial decisions: April 1, 2026
Submission of full-length chapters: July 1, 2026
Notification of final editorial decisions: August 15, 2026
Submission of revised chapters: October 1, 2026
Publication of the volume: Late 2026

Submission Instructions
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Each contributed chapter is expected to present a novel research study, a comparative study, or a survey of the literature. All submissions, including abstracts and full-length chapters, must be done via EasyChair: https://easychair.org/conferences/?conf=cbc2026

There will be no publication fees for the accepted chapters.

Resources for chapter authors
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Book publishing policies: https://tinyurl.com/2r66erx9
Manuscript guidelines: https://tinyurl.com/yermksud
Book chapter template (Microsoft Word): https://tinyurl.com/47js3ms6
Book chapter template (LaTeX): https://tinyurl.com/y43xdztx
LaTeX best practice guidelines: https://tinyurl.com/yzsp3472
Sample chapter: https://tinyurl.com/2nnbf64a

About the Editor
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M. Emre Celebi (https://tinyurl.com/mtw6dx39) received his B.S. degree in Computer Engineering from the Middle East Technical University (Ankara, Turkey) in 2002. He received his M.S. and Ph.D. degrees in Computer Science and Engineering from the University of Texas at Arlington (Arlington, TX, USA) in 2003 and 2006, respectively. He is currently a Professor and the Chair of the Department of Computer Science and Engineering at the University of Central Arkansas.

Celebi has actively pursued research in artificial intelligence and image processing with an emphasis on medical image analysis, color image processing, and partitional clustering. He has worked on several projects funded by the US National Science Foundation and the US National Institutes of Health, publishing 180 articles in reputable journals and conference proceedings. As of January 2026, his work has received over 22,000 citations with an h-index of 63 (GS: https://tinyurl.com/3snjm8at). According to a 2025 Stanford University study (https://tinyurl.com/2f24dfrp), based on the composite citation index (an indicator of citation impact), in the single-year (2024) and career-long impact categories, Celebi ranked 873rd and 893rd, respectively, out of 458,615 artificial intelligence and image processing researchers worldwide.

Celebi has published 12 edited volumes since 2012, all but one with Springer Nature. The artificial intelligence/machine learning volumes he edited to date include Partitional Clustering Algorithms (Springer Nature, 2015, https://tinyurl.com/3yezehcw) and Unsupervised Learning Algorithms (Springer Nature, 2016, https://tinyurl.com/4z8hdevh). He is also a series editor of two Springer Nature book series: Unsupervised and Semi-Supervised Learning (since 2017) and Signals and Communication Technology (since 2019).

Contact
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Feel free to contact us via email (ecelebi AT uca DOT edu) with your chapter ideas.