We are pleased to invite you to contribute a chapter to our upcoming edited volume, Object 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

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

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

• 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

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

• 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

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

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