Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index

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Introduction to Data Mining

International Journal of Computer and Applications

This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. This paper proposes the solution of these problems. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. The results show that proposed model improves the accuracy and minimal error. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. The survey has been made for the fixation of hidden neurons in neural networks. The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks.

/pdf/review-on-methods-to-fix-number-of-hidden-neurons-in-neural-22240krax1.pdf

Review on Methods to Fix Number of Hidden Neurons in Neural Networks

Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Introduction to Fuzzy Logic using MATLAB

Introduction to Neural Networks Using Matlab 6.0

Offering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and TakagiSugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization. Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

Computational Intelligence Paradigms: Theory & Applications using MATLAB

to Data Mining Principles- Data Warehousing, Data Mining, and OLAP- Data Marts and Data Warehouse- Evolution and Scaling of Data Mining Algorithms- Emerging Trends and Applications of Data Mining- Data Mining Trends and Knowledge Discovery- Data Mining Tasks, Techniques, and Applications- Data Mining: an Introduction - Case Study- Data Mining & KDD- Statistical Themes and Lessons for Data Mining- Theoretical Frameworks for Data Mining- Major and Privacy Issues in Data Mining and Knowledge Discovery- Active Data Mining- Decomposition in Data Mining - A Case Study- Data Mining System Products and Research Prototypes- Data Mining in Customer Value and Customer Relationship Management- Data Mining in Business- Data Mining in Sales Marketing and Finance- Banking and Commercial Applications- Data Mining for Insurance- Data Mining in Biomedicine and Science- Text and Web Mining- Data Mining in Information Analysis and Delivery- Data Mining in Telecommunications and Control- Data Mining in Security

S. Sumathi

Papers

Introduction to Fuzzy Logic using MATLAB

Introduction to Neural Networks Using Matlab 6.0

Computational Intelligence Paradigms: Theory & Applications using MATLAB

Introduction to data mining and its applications

Solar PV and Wind Energy Conversion Systems