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Meechie Chow
Meechie Chow

Data Mining, Fourth Edition: Practical Machine Learning Tools And Techniques (Morgan Kaufmann Series



Machine learning provides an exciting set of technologiesthat includes practical tools for analyzing data and makingpredictions but also powers the latest advances inartificial intelligence. We have written a book thatprovides a highly accessible introduction to the area butalso caters for readers who want to delve into the moremathematical techniques available in modern probabilisticmodeling and deep learningapproaches. ChrisPal hasjoined IanWitten, EibeFrank,and MarkHall for the fourth edition, and his expertise inprobabilistic models and deep learning has greatly extendedthe book's coverage. To make room for the new material, wenow provide an online appendix on the Weka software. It isan extended version of a brief description of Weka includedas an appendix in the book. The book continues to providereferences to Weka implementations of algorithms that itdescribes. The WekaMOOCs provide activities similar to the tutorialexercises in the 3rd edition. We now also provide information on othersoftware: the computational ecosystem for machine learninghas grown enormously since we have written the third editionin 2011. A table of contents for thefourth edition, indicating where we have added new material,can be found further down this page.




Data Mining, Fourth Edition: Practical Machine Learning Tools And Techniques (Morgan Kaufmann Series



In the early 1990s some sectors of the computer science community were developing the idea of data understanding as a discovery-driven, systematic and iterative process. This "data mining" research and development area was expected to take advantage of the expansion and consolidation of machine learning methodologies together with the integration of traditional statistical analysis and database management strategies. The main goal was to identify relevant, interesting and potentially novel informational patterns and relationships in large data sets to support decision making and knowledge discovery. In the mid 1990s developers and users of decision-making support systems in areas such as finance (e.g. credit approval and fraud detection applications), marketing and sales analysis (e.g. shopping patterns and sales prediction) were showing a great deal of enthusiasm about the business value of data mining applications. During the next few years international conferences, journals and books were more frequently reporting advances, tools and applications in other areas such as biomedical informatics, engineering, physics, law enforcement and agriculture. Today data mining is seen as a discipline or paradigm that actively aids in the development of these and other scientific areas (e.g. Web-based computing and systems biology).


In "Data Mining: Practical Machine Learning Tools and Techniques" Witten and Frank offer users, students and researchers alike a balanced, clear introduction to concepts, techniques and tools for designing, implementing and evaluating data mining applications. Although it puts emphasis on machine learning techniques, it also introduces basic statistical and information representation methods. This book provides a variety of simple yet elegant explanations to guide the reader to understand essential concepts and approaches. The book can also be seen as a well-structured, intensive tutorial, which excels in explaining how to implement solutions to different problems.


Another reason why this book represents a significant contribution to this area is the ability of the authors to bridge gaps between conceptual and theoretical discussions, methods and practical implementations. Obviously it would not be possible (or necessary) to cover, in a single book, all the range of problems and machine learning techniques applied to different domains. However, this book also succeeds in organising and summarising significant amounts of material useful to assist the reader in justifying the selection of specific solutions. This is accomplished without making exaggerated claims or oversimplifying fundamental definitions.


The book is divided into two parts. The first part consists of eight chapters introducing machine learning methods, data pre-processing, model evaluation and practical implementations. An important feature is the presentation of different techniques to evaluate model predictive quality and to compare different models (e.g. cross-validation methods, probability estimations, receiver operating characteristic curves). Decision trees, different classification rule methods, instance-based learning models and Bayesian networks are some of the machine learning techniques introduced. The second part focuses on the Weka system, which offers three graphical user interfaces: the Explorer, the Knowledge Flow Interface and the Experimenter. In comparison to its first edition, some of the improvements include more information on neural networks and kernel models, as well as new (or updated) sections on methods, technical challenges and additional reading.


"Data Mining: Practical Machine Learning Tools and Technique" may become a key reference to any student, teacher or researcher interested in using, designing and deploying data mining techniques and applications. This book also deals with various aspects relevant to undergraduate or research programmes in machine learning, intelligent systems, bioinformatics and biomedical informatics.


Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.


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