The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

/pdf/statistical-pattern-recognition-a-review-1kz1b0tte4.pdf

Statistical pattern recognition: a review

https://espace.library.uq.edu.au/view/UQ:8925/pr-t.pdf

The use of the area under the ROC curve in the evaluation of machine learning algorithms

■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

/pdf/from-data-mining-to-knowledge-discovery-in-databases-4fznxuujb9.pdf

From Data Mining to Knowledge Discovery in Databases

may 7th, 1986, Professor A. F. M. Smith in the Chair] SUMMARY A continuous two-dimensional region is partitioned into a fine rectangular array of sites or "pixels", each pixel having a particular "colour" belonging to a prescribed finite set. The true colouring of the region is unknown but, associated with each pixel, there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model. The aim is to reconstruct the true scene, with the additional knowledge that pixels close together tend to have the same or similar colours. In this paper, it is assumed that the local characteristics of the true scene can be represented by a nondegenerate Markov random field. Such information can be combined with the records by Bayes' theorem and the true scene can be estimated according to standard criteria. However, the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field. Thus, a simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics. The method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field. Some complications, including parameter estimation, are discussed. Potential applications are mentioned briefly.

https://stat.duke.edu/~scs/Courses/Stat376/Papers/GibbsFieldEst/BesagDirtyPicsJRSSB1986.pdf

On the statistical analysis of dirty pictures

Preface 1 Overview of Learning Systems 1.1 What is a Learning System? 1.2 Motivation for Building Learning Systems 1.3 Types of Practical Empirical Learning Systems 1.3.1 Common Theme: The Classification Model 1.3.2 Let the Data Speak 1.4 What's New in Learning Methods 1.4.1 The Impact of New Technology 1.5 Outline of the Book 1.6 Bibliographical and Historical Remarks 2 How to Estimate the True Performance of a Learning System 2.1 The Importance of Unbiased Error Rate Estimation 2.2. What is an Error? 2.2.1 Costs and Risks 2.3 Apparent Error Rate Estimates 2.4 Too Good to Be True: Overspecialization 2.5 True Error Rate Estimation 2.5.1 The Idealized Model for Unlimited Samples 2.5.2 Train-and Test Error Rate Estimation 2.5.3 Resampling Techniques 2.5.4 Finding the Right Complexity Fit 2.6 Getting the Most Out of the Data 2.7 Classifier Complexity and Feature Dimensionality 2.7.1 Expected Patterns of Classifier Behavior 2.8 What Can Go Wrong? 2.8.1 Poor Features, Data Errors, and Mislabeled Classes 2.8.2 Unrepresentative Samples 2.9 How Close to the Truth? 2.10 Common Mistakes in Performance Analysis 2.11 Bibliographical and Historical Remarks 3 Statistical Pattern Recognition 3.1 Introduction and Overview 3.2 A Few Sample Applications 3.3 Bayesian Classifiers 3.3.1 Direct Application of the Bayes Rule 3.4 Linear Discriminants 3.4.1 The Normality Assumption and Discriminant Functions 3.4.2 Logistic Regression 3.5 Nearest Neighbor Methods 3.6 Feature Selection 3.7 Error Rate Analysis 3.8 Bibliographical and Historical Remarks 4 Neural Nets 4.1 Introduction and Overview 4.2 Perceptrons 4.2.1 Least Mean Square Learning Systems 4.2.2 How Good Is a Linear Separation Network? 4.3 Multilayer Neural Networks 4.3.1 Back-Propagation 4.3.2 The Practical Application of Back-Propagation 4.4 Error Rate and Complexity Fit Estimation 4.5 Improving on Standard Back-Propagation 4.6 Bibliographical and Historical Remarks 5 Machine Learning: Easily Understood Decision Rules 5.1 Introduction and Overview 5.2 Decision Trees 5.2.1 Finding the Perfect Tree 5.2.2 The Incredible Shrinking Tree 5.2.3 Limitations of Tree Induction Methods 5.3 Rule Induction 5.3.1 Predictive Value Maximization 5.4 Bibliographical and Historical Remarks 6 Which Technique is Best? 6.1 What's Important in Choosing a Classifier? 6.1.1 Prediction Accuracy 6.1.2 Speed of Learning and Classification 6.1.3 Explanation and Insight 6.2 So, How Do I Choose a Learning System? 6.3 Variations on the Standard Problem 6.3.1 Missing Data 6.3.2 Incremental Learning 6.4 Future Prospects for Improved Learning Methods 6.5 Bibliographical and Historical Remarks 7 Expert Systems 7.1 Introduction and Overview 7.1.1 Why Build Expert Systems? New vs. Old Knowledge 7.2 Estimating Error Rates for Expert Systems 7.3 Complexity of Knowledge Bases 7.3.1 How Many Rules Are Too Many? 7.4 Knowledge Base Example 7.5 Empirical Analysis of Knowledge Bases 7.6 Future: Combined Learning and Expert Systems 7.7 Bibliographical and Historical Remarks References Author Index Subject Index

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems

https://people.dbmi.columbia.edu/~ehs7001/Clancey-Shortliffe-1984/Ch7.pdf

A model-based method for computer-aided medical decision-making

http://bionmr.unl.edu/files/publications/19.pdf

Automated analysis of protein NMR assignments using methods from artificial intelligence

This book offers a practical introduction to expert systems and is designed not only for computer programmers but for all those who want to know how expert systems are structured and what they can do.

A Practical Guide to Designing Expert Systems

The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.

/pdf/robust-and-fast-collaborative-tracking-with-two-stage-sparse-4h5hpcih4h.pdf

Casimir A. Kulikowski

Papers

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems

A model-based method for computer-aided medical decision-making

Automated analysis of protein NMR assignments using methods from artificial intelligence

A Practical Guide to Designing Expert Systems

Robust and fast collaborative tracking with two stage sparse optimization