The importance of data mining has grown greatly over recent decades due to evolutionary progress in computing resources, as well as growing complexities in business, ecology, and environment. The quest for knowledge is age old and has encouraged mankind to use historical datasets, mapping techniques through tacit and explicit application of mind and various techniques including algorithms. There are established algorithms used today in statistics, optimization, control, data mining, and machine learning to extract knowledge and information for various uses. However, there are debates and research on exploring and adopting successful, efficient, and productive algorithms for managing huge datasets with complex dimensions. The emergence of huge data generated by real-time, online, and offline modes of transactions has made the situation more complex. This complexity has also grown a great deal with volume and noise because of sources like Internet enterprise applications, the Internet of Things, and geospatial applications and services. Therefore, data mining has been quite a challenging area of research. However, the research and applications in the areas of data mining and data warehousing have been generating interesting insights while formalizing algorithms and techniques including deterministic and stochastic approaches.

This book carefully and broadly covers aspects related to data, data mining, information, and knowledge. The reader will find it very interesting to go through the evolutionary path that the field of data mining has traversed. Each area of data mining is clearly defined in this book, supported by algorithms and small examples. But the book has been grossly limited to broad scientific-environment-based algorithms and mathematical models. It is overdue to see and experience this topic through real-life examples across all domains, including the business, geospatial, scientific, and development sectors. This book had the opportunity to cover all of these aspects. The algorithms presented and discussed in the book are focused on randomized and clustering types. These discussions could have included areas related to comparative and benchmarked situations with numeric, text, and composite mining algorithms. It is known that the contemporary computing environment is focused on the Internet of Things, and distributed and Internet-enabled applications. This requires more focus on architectures than algorithms. The book is silent on architectural studies.

Overall, however, the book will generate interest in researchers involved in data structures and algorithms. With regard to practitioners, this book will be a disappointment because it can be difficult to read, it lacks descriptive and comparative examples available in real-life situations with time benchmarks, and the material is complex.