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• AN OVERVIEW OF DATA MINING METHODOLOGIES Introduction to data mining methodologies METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS Regression models Bayes classifiers Decision trees Multi-layer feedforward artificial neural networks Support vector machines Supervised clustering METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS Hierarchical clustering Partitional clustering Self-organized map Probability distribution estimation Association rules Bayesian networks METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS Principal components analysis Multi-dimensional scaling Latent variable analysis METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS Univariate control charts Multivariate control charts METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS Autocorrelation based time series analysis Hidden Markov models for sequential pattern mining Wavelet analysis Hilbert transform Nonlinear time series analysis.
• (source: Nielsen Book Data)

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various dat.
(source: Nielsen Book Data)
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.
(source: Nielsen Book Data)

• 1. Specification mining : a concise introduction / David Lo [and others]
• 2. Mining finite-state automata with annotations / Leonardo Mariani [and others]
• 3. Adapting grammar inference techniques to mine state machines / Neil Walkinshaw and Kirill Bogdanov
• 4. Mining API usage protocols from large methods traces / Michael Pradel and Thomas R. Gross
• 5. Static API specification mining : exploiting source code model checking / Mithun Acharya and Tao Xie
• 6. Static specification mining using automata-based abstractions / Eran Yahav [and others]
• 7. DynaMine : finding usage patterns and their violations by mining software repositories / Benjamin Livshits and Thomas Zimmermann
• 8. Automatic inference and effective application of temporal specifications / Jinlin Yang and David Evans
• 9. Path-aware static program analyses for specification mining / Muralikrishna Ramanathan, Ananth Grama, and Suresh Jagannathan
• 10. Mining API usage specifications via searching source code from the Web / Suresh Thummalapenta, Tao Xie, and Madhuri R. Marri
• 11. Merlin : specification inference for explicit information flow problems / Benjamin Livshits [and others]
• 12. Lightweight mining of object usage / Andrzej Wasylkowski and Andreas Zeller

• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
• (source: Nielsen Book Data)

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
(source: Nielsen Book Data)

• Introduction and Overview of the Main Results of the Book.- Basic concepts of data stream mining.- Decision Trees in Data Stream Mining.- Splitting Criteria based on the McDiarmid's Theorem.
• (source: Nielsen Book Data)

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.
(source: Nielsen Book Data)