Springer’s Unsupervised and Semi-Supervised Learning book series covers the latest theoretical and practical developments in unsupervised and semi-supervised learning. Titles---including monographs, contributed works, professional books, and textbooks---tackle various issues surrounding the proliferation of massive amounts of unlabeled data in many application domains and how unsupervised learning algorithms can automatically discover interesting and useful patterns in such data. The books discuss how these algorithms have found numerous applications, including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. Books also discuss semi-supervised algorithms, which can make use of both labeled and unlabeled data and can be useful in application domains where unlabeled data is abundant, yet it is possible to obtain a small amount of labeled data.

Topics of interest include:
- Unsupervised/Semi-Supervised Deep Learning
- Unsupervised/Semi-Supervised Discretization
- Unsupervised/Semi-Supervised Feature Extraction
- Unsupervised/Semi-Supervised Feature Selection
- Association Rule Learning
- Semi-Supervised Classification
- Semi-Supervised Regression
- Unsupervised/Semi-Supervised Clustering
- Unsupervised/Semi-Supervised Anomaly/Novelty/Outlier Detection
- Evaluation of Unsupervised/Semi-Supervised Learning Algorithms
- Applications of Unsupervised/Semi-Supervised Learning

While the series focuses on unsupervised and semi-supervised learning, outstanding contributions in supervised learning (e.g., deep learning) will also be considered. The intended audience includes students, researchers, and practitioners.

Indexed in: zbMATH Open (formerly known as Zentralblatt MATH)
Electronic ISSN: 2522-8498
Print ISSN: 2522-848X
Series Editor: M. Emre Celebi, Ph.D. (ecelebi@uca.edu)
Publishing Editor: Mary James (mary.james@springer.com)
Book Proposal Form: https://media.springer.com/full/springer-instructions-for-authors-assets/pdf/SN_BPF_EN.pdf

Book Titles in This Series (16)
- Feature and Dimensionality Reduction for Clustering with Deep Learning (Frederic Ros & Rabia Riad, 2024)
- Partitional Clustering via Nonsmooth Optimization, Second Edition (Adil Bagirov, Napsu Karmitsa & Sona Taheri, 2025)
- Super-Resolution for Remote Sensing (Michal Kawulok, Jolanta Kawulok, Bogdan Smolka & M. Emre Celebi, 2024)
- Unsupervised Feature Extraction Applied to Bioinformatics, Second Edition (Y-h. Taguchi, 2024)
- Advances in Computational Logistics and Supply Chain Analytics (Ibraheem Alharbi, Chiheb-Eddine Ben Ncir, Bader Alyoubi & Hajer Ben-Romdhane, 2024)
- Machine Learning and Data Analytics for Solving Business Problems (Bader Alyoubi, Chiheb-Eddine Ben Ncir, Ibraheem Alharbi & Anis Jarboui, 2022)
- Hidden Markov Models and Applications (Nizar Bouguila, Wentao Fan & Manar Amayri, 2022)
- Deep Biometrics (Richard Jiang, Chang-Tsun Li, Danny Crookes, Weizhi Meng & Christophe Rosenberger, 2020)
- Partitional Clustering via Nonsmooth Optimization (Adil M. Bagirov, Napsu Karmitsa & Sona Taheri, 2020)
- Sampling Techniques for Supervised or Unsupervised Tasks (Frédéric Ros & Serge Guillaume, 2020)
- Supervised and Unsupervised Learning for Data Science (Michael W. Berry, Azlinah Mohamed & Bee Wah Yap, 2020)
- Unsupervised Feature Extraction Applied to Bioinformatics (Y-h. Taguchi, 2020)
- Mixture Models and Applications (Nizar Bouguila & Wentao Fan, 2020)
- Linking and Mining Heterogeneous and Multi-view Data (Deepak P & Anna Jurek-Loughrey, 2019)
- Clustering Methods for Big Data Analytics (Olfa Nasraoui & Chiheb-Eddine Ben N'Cir, 2019)
- Natural Computing for Unsupervised Learning (Xiangtao Li & Ka-Chun Wong, 2019)