Dear Colleagues,

We are pleased to invite you to contribute a chapter to our upcoming edited volume, Objective Function-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 2027 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 across 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, objective function-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 optimization-based formulation and computational efficiency.

This volume aims to summarize the state of the art in objective function-based clustering.

Topics of Interest
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Topics of interest include:
Hard clustering algorithms
Fuzzy, probabilistic, and possibilistic clustering algorithms
Information-theoretic clustering algorithms
Neural clustering algorithms
Metaheuristic clustering algorithms
Constrained clustering algorithms
Approximate clustering algorithms
Parallel and distributed clustering algorithms
Dissimilarity and 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 and hardware implementations of clustering algorithms
Applications of clustering algorithms

Important dates
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Submission of abstracts: February 1, 2027
Notification of initial editorial decisions: February 15, 2027
Submission of full-length chapters: May 15, 2027
Notification of final editorial decisions: July 1, 2027
Submission of revised chapters: October 1, 2027
Publication of the volume: Late 2027

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=ofbc2027

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/mpcd9ype
Book chapter templates:
Microsoft Word: https://tinyurl.com/4swdmd3x
LaTeX: https://tinyurl.com/jbyz5nx2
LaTeX (Overleaf): https://tinyurl.com/22sk4aec
LaTeX best practice guidelines: https://tinyurl.com/2zvaje5f

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 pursued research in artificial intelligence and image processing, focusing 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 over 180 articles in reputable journals and conference proceedings. As of October 2026, his work has received nearly 25,000 citations with an h-index of 65 (GS: https://tinyurl.com/3snjm8at). According to a 2026 Stanford University study (https://tinyurl.com/236hx3m2), based on the composite citation index (an indicator of citation impact), in the single-year (2025) and career-long impact categories, Celebi ranked 1277th and 942nd, respectively, out of 517,648 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.