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Data Mining: Practical Machine Learning Tools and TechniquesJanuary 2011
- Authors:
- Ian H. Witten,
- Eibe Frank,
- Mark A. Hall
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Abstract
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
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- Mporas I, Tsirka V, Zacharaki E, Koutroumanidis M and Megalooikonomou V Evaluation of time and frequency domain features for seizure detection from combined EEG and ECG signals Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, (1-4)
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- Knauss E and Ott D Semi- automatic Categorization of Natural Language Requirements Proceedings of the 20th International Working Conference on Requirements Engineering: Foundation for Software Quality - Volume 8396, (39-54)
- Thesprasith O and Jaruskulchai C Query Expansion Using Medical Subject Headings Terms in the Biomedical Documents Proceedings, Part I, of the 6th Asian Conference on Intelligent Information and Database Systems - Volume 8397, (93-102)
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- Rodriguez D, Herraiz I and Harrison R On software engineering repositories and their open problems Proceedings of the First International Workshop on Realizing AI Synergies in Software Engineering, (52-56)
- Xuan J, Jiang H, Ren Z and Zou W Developer prioritization in bug repositories Proceedings of the 34th International Conference on Software Engineering, (25-35)
- Xu B, Huang J, Williams G, Li M and Ye Y Hybrid random forests Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I, (147-158)
- Deligianni D and Kotsiantis S Forecasting corporate bankruptcy with an ensemble of classifiers Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications, (65-72)
- Zouboulidis E and Kotsiantis S Forecasting fraudulent financial statements with committee of cost-sensitive decision tree classifiers Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications, (57-64)
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- Assam R and Seidl T Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, (9-16)
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- Stolpe M and Morik K Learning from label proportions by optimizing cluster model selection Proceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III, (349-364)
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- Moreira-Matias L and Cerqueira V CJAMmer - traffic JAM Cause Prediction using Boosted Trees 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), (743-748)
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- Tran C, Zhang M and Andreae P Directly evolving classifiers for missing data using genetic programming 2016 IEEE Congress on Evolutionary Computation (CEC), (5278-5285)
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- Piantadosi G, Fusco R, Petrillo A, Sansone M and Sansone C LBP-TOP for Volume Lesion Classification in Breast DCE-MRI Image Analysis and Processing — ICIAP 2015, (647-657)
- Yuan X, Sa N, Begany G and Yang H What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization Human-Computer Interaction – INTERACT 2015, (249-264)
Index Terms
- Data Mining: Practical Machine Learning Tools and Techniques
Reviews
Radu State
When the authors of the Waikato Environment for Knowledge Analysis (Weka), a well-known and widely used data mining toolkit, teamed up to write a book on data mining techniques, the expectations were high. This third edition meets these expectations by combining hands-on and application-driven content with thorough coverage of the technical background of data mining and machine learning. The book is logically divided into three major sections. Part 1 (the first five chapters) is a solid introduction to basic data mining algorithms and real-life data processing. With regard to content, this part addresses the basic association mining algorithms, automated clustering techniques, and regression-based classification. From an operational point of view, the authors address the important and frequent issue of dealing with missing and/or inaccurate data items. Part 2 (the next four chapters) leverages the techniques from the first part. The authors refine them, cover more advanced data mining approaches, and consider the fine-tuning of previously described data clustering techniques. In this second part, the authors: show how the numbers of clusters in a clustering algorithm can be set; address the co-training learning mechanism; and delve into the more recent techniques of bagging and boosting. A dedicated chapter addresses the transformation and relevance-driven selection of attributes. It also discusses popular data transformation techniques such as principal component analysis (PCA). The final part of the book (chapters 10 to 17) provides an in-depth introduction to the Weka toolkit. The authors not only focus on the simple usage of Weka, but also give a comprehensive overview on extending the Weka toolkit with custom libraries and integrating Weka into an embedded data mining application. Chapter 15"?my favorite"?thoroughly covers how to build a standalone data mining application on top of the Weka libraries. This book is a must-read for every aspiring data mining analyst. Its many examples and the technical background it imparts would be a unique and welcome addition to the bookshelf of any graduate or advanced undergraduate student. The book is written for both academic and application-oriented readers, and I strongly recommend it to any reader working in the area of machine learning and data mining. Online Computing Reviews Service
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