This open access book answers some of the hard questions in the field of machine learning and data mining. What if one of the most challenging problems in machine learning—clustering in high‑dimensional, complex data—could be solved not with deep learning but through simple space partitioning in linear time. It introduces a groundbreaking family of isolation‑based algorithms, from the widely adopted Isolation Forest to the more recent Isolation Kernel (IK) and Isolation Distributional Kernel (IDK), along with many new methods derived from them. Together, these approaches enable effective anomaly detection, clustering, classification, and similarity search across vector databases and complex data types such as time series, trajectories, and graphs.

Designed for machine learning and data mining researchers, data scientists, and professionals working with large or structured datasets, the book demonstrates how isolation partitions—created by isolating each point to extract distributional information from small samples—can outperform sophisticated learning‑based techniques, including deep learning, in both speed and accuracy. It presents a compelling case that clustering, traditionally considered NP‑hard, can be solved optimally in linear time through isolation‑inspired thinking, without the limitations of k‑means, Spectral Clustering, or Deep Clustering.

Beyond algorithmic innovation, the book emphasizes intuitive insights and lessons learned over eighteen years of research. It shows why understanding a problem deeply is often the key to simpler, better solutions, challenging the assumption that “deep learning is the answer.” With minimal prerequisites, it invites a broad range of readers to explore how isolation‑inspired methods can redefine problem formulation and solution efficiency in machine learning.

Kai Ming Ting is a Full Professor at the School of Artificial Intelligence, Nanjing University. He is best known for developing Isolation Forest, a widely adopted anomaly detection algorithm, which has more than 10,000 citations in either scholar.google.com or patents.google.com, and for introducing the Isolation Kernel and Isolation Distributional Kernel (IDK). These isolation-based methods have transformed data mining and machine learning by enabling fast, effective solutions to tasks such as anomaly detection, clustering, and retrieval in vector databases as well as complex data types including time series, trajectories, graphs, and spatial transcriptomics. Notably, IDK-based methods often outperform deep learning models while running efficiently on CPUs. Professor Ting has served on program committees for leading AI and data mining conferences such as AAAI, ACM SIGKDD, IEEE ICDM, and ICML, and has received research grants from the NSFC, US Air Force, Australian Research Council, and Toyota InfoTechnology Center. His work has earned accolades including the ACM SIGKDD ANDEA Test of Time Award and IEEE ICDM best paper award.