Best Reference Books – Statistical Techniques in Data Mining

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We have compiled the list of Top 10 Best Reference Books on Statistical Techniques In Data Mining subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Statistical Techniques In Data Mining along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Statistical Techniques In Data Mining subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Statistical Techniques In Data Mining" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Classification of Regression Trees” by L Breiman and C J Stone
2. “Principles of Data Mining” by D J Hand and P Smith

“Principles of Data Mining” Book Review: This book covers the core principles of data mining. main topics introduced measurement in data, visualising exploring data, data analysis and uncertainty. Other topics included are A systematic overview of data mining algorithms, models and patterns, search and optimization methods, descriptive modelling. List of tables and list of figures are provided at the start of the book. All the algorithms and programs are discussed in detail. This book is useful for undergraduate computer science and information technology students.

3. “Fundamentals of Artificial Neural Networks” by M H Hassoun

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“Fundamentals of Artificial Neural Networks” Book Review: This book discusses the fundamentals of artificial neural networks. Main topics included are Threshold gates, computational capabilities of artificial neural networks, learning rules. Other topics such as mathematical theory of neural learning, adaptive multilayer neural networks, associative neural memories are also discussed. Over 200 computer-based problems and 700 references are added in this book. Numerous illustrative examples are also provided to make the topics easily understandable. Students studying computer science and information technology engineering can refer to this book.

4. “The Elements of Statistical Learning: Data Mining, Inference and Prediction” by T Hastie and J H Friedman

“The Elements of Statistical Learning: Data Mining, Inference and Prediction” Book Review: This book includes the main elements of statistical learning. main topics namely overview of supervised learning, linear methods for regression, linear methods for classification are introduced. Other topics mentioned are basic expansions and regularisation kernel methods, model assessment and selection. Exercises are also added at the end of each chapter for student’s practice. This book is useful for computer engineering students.

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5. “Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions” by Nuno M.M. Ramos and João M.P.Q. Delgado

“Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions” Book Review: This book discusses data mining techniques used in the analysis of indoor hygrothermal conditions. Main topics introduced are indoor hygrothermal conditions, descriptive statistics, multiple regression analysis, classification trees. Other topics included are Case studies on outdoor conditions, in temperature, indoor relative humidity and applications of data mining techniques. All the techniques and methods are described with proper programs. This book is suitable for advanced computer science engineering students.

6. “Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding” by Alessandro Fiori

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“Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding” Book Review: This book discusses the innovative document summarization techniques for information retrieval. Main topics included are classification of sentence ranking method, multi document summarization, efficient summarization with polytopes. Other topics included are Evaluation matrices for summarization tasks, interactive summaries by multi pole information extraction. The main aim of this book is to Create algorithms and methods to effectively find out information without searching huge and relevant databases. This book is useful for advanced computer science engineering students.

7. “Computer Based Numerical and Statistical Techniques” by M Goyal

“Computer Based Numerical and Statistical Techniques” Book Review: This book covers computer based numerical and statistical techniques. Topics such as computer arithmetic and errors, roots of equations, calculus of finite differences, interpolation are involved in this book. Other topics mentioned are Piecewise and spline interpolation, approximation of functions, numerical differentiation, numerical integration. This book provides software-based examples, flow charts and applications to easily understand the concept. The main motive behind this book is to get answers to difficult mathematical problems by software rather than solving the problems by yourself. This book is useful for advanced engineering students.

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8. “Research Methods and Statistical Techniques” by P S G Kumar
9. “A Textbook of Computer Based Numerical and Statistical Techniques” by A K Jaiswal and Anju Khandelwal

“A Textbook of Computer Based Numerical and Statistical Techniques” Book Review: This book describes the application of numerical analysis and statistical techniques. Topics such as Partial differential equations of first order and second order, spline interpolation, Numerical integration and differentiation are discussed. The aim of this book is to create such programs that all the mathematical problems can be solved by the computer. Numerical problems end programs are also discussed in this book which are written in C language. It also provides error analysis for almost all the methods. Students studying graduate level computer science engineering can refer to this book.

10. “Computer-Based Numerical and Statistical Techniques” by P K De

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“Computer-Based Numerical and Statistical Techniques” Book Review: This book is an introduction to numerical analysis and statistical techniques. main topics included are Advanced numerical methods of finite element method, introductory level matlab concepts and partial differential equations. Extensive exercises and many computer programs are also added in this book for student’s practice. Matlab programs and C language programs are also added in this book. This book is beneficial for graduate level students of computer science Engineering and computational mathematics.

People who are searching for Free downloads of books and free pdf copies of these top 10 books on Statistical Techniques In Data Mining – we would like to mention that we don’t have free downloadable pdf copies of these good books and one should look for free pdf copies from these Authors only if they have explicitly made it free to download and read them.

We have created a collection of best reference books on "Statistical Techniques In Data Mining" so that one can readily see the list of top books on "Statistical Techniques In Data Mining" and buy the books either online or offline.

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter