Introduction to Data Mining by Tan, Steinbach and Kumar: A Comprehensive Guide
Data mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, text documents, images, videos, web pages, and more. Data mining can help organizations and individuals to gain insights, make predictions, and improve decision making.
One of the most popular and widely used textbooks on data mining is Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar. The book covers both theoretical and practical aspects of data mining, with topics ranging from classification, association analysis, clustering, anomaly detection, to avoiding false discoveries. The book also provides many examples, figures, and exercises to help readers understand and apply the data mining techniques.
The book has two editions: the first edition was published in 2005 and the second edition was published in 2018. The second edition has been updated and expanded with new topics such as deep networks, one-class classification, information-theoretic anomaly detection, and more. The book also has a companion website that provides additional resources for instructors and students, such as lecture slides, Python notebooks, R code examples, solution manual, and question bank.
However, the book is not freely available online. Some people may try to find a torrent or a rar file that contains a copy of the book, but this is illegal and unethical. The authors have spent a lot of time and effort to write this book and deserve to be compensated for their work. Moreover, downloading a torrent or a rar file may expose the user to viruses or malware that can harm their computer or compromise their privacy.
Therefore, the best way to access the book is to buy it from a legitimate source such as Pearson or Amazon. Alternatively, one can borrow it from a library or a friend who has a copy. This way, one can enjoy the benefits of learning from this excellent book without violating any laws or moral principles.
In conclusion, Introduction to Data Mining by Tan, Steinbach and Kumar is a comprehensive and authoritative book that covers the theory and practice of data mining. It is suitable for students, researchers, and practitioners who want to learn the fundamentals and applications of data mining. The book is available in two editions, with the second edition being more updated and expanded. The book can be purchased from reputable sources or borrowed from libraries or friends. Downloading a torrent or a rar file of the book is not only illegal and unethical, but also risky and unreliable.
If you are interested in learning more about data mining, you may also want to check out some of the other books and online courses that are available on this topic. For example, Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei is another popular textbook that covers the core concepts and methods of data mining. It also has a companion website that provides lecture slides, datasets, and software tools.
Another option is to enroll in an online course that teaches data mining. There are many platforms that offer such courses, such as Coursera, edX, Udemy, and DataCamp. These courses usually have video lectures, quizzes, assignments, and projects that help you learn and practice data mining skills. Some of the courses may also provide a certificate of completion or a credential that you can add to your resume or portfolio.
Data mining is a fascinating and useful field that can help you discover patterns and insights from large and complex data. Whether you want to pursue a career in data science, enhance your current skills, or simply satisfy your curiosity, there are many resources that can help you learn data mining. However, you should always respect the intellectual property rights of the authors and creators of these resources and avoid downloading or sharing illegal copies of their work. aa16f39245