Data Mining Mehmed Kantardzic (good english books to read .txt) 📖

Webb, Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining, Journal of Machine Learning Research, Vol. 10, 2009, p. 377–403.

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches.

Mitchell, T., Machine Learning, McGraw Hill, New York, NY, 1997.

This is one of the most comprehensive books on machine learning. Systematic explanations of all methods and a large number of examples for all topics are the main strengths of the book. Inductive machine-learning techniques are only a part of the book, but for a deeper understanding of current data-mining technology and newly developed techniques, it is very useful to get a global overview of all approaches in machine learning.

Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1992.

The book outlines the C4.5 algorithm step by step, with detailed explanations and many illustrative examples. The second part of the book is taken up by the source listing of the C program that makes up the C4.5 system. The explanations in the book are intended to give a broad-brush view of C4.5 inductive learning with many small heuristics, leaving the detailed discussion to the code itself.

Russell, S., P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, NJ, 1995.

The book gives a unified presentation of the artificial intelligence field using an agent-based approach. Equal emphasis is given to theory and practice. An understanding of the basic concepts in artificial intelligence (AI), including an approach to inductive machine learning, is obtained through layered explanations and agent-based implementations of algorithms.

7

ARTIFICIAL NEURAL NETWORKS

Chapter Objectives

Identify the basic components of artificial neural networks (ANNs) and their properties and capabilities.

Describe common learning tasks such as pattern association, pattern recognition, approximation, control, and filtering that are performed by ANNs.

Compare different ANN architecture such as feedforward and recurrent networks, and discuss their applications.

Explain the learning process at the level of an artificial neuron, and its extension for multiplayer, feedforward-neural networks.

Compare the learning processes and the learning tasks of competitive networks and feedforward networks.

Present the basic principles of Kohonen maps and their applications.

Discuss the requirements for good generalizations with ANNs based on heuristic parameter tuning.

Work on ANNs has been motivated by the recognition that the human brain computes in an entirely different way from the conventional digital computer. It was a great challenge for many researchers in different disciplines to model the brain’s computational processes. The brain is a highly complex, nonlinear, and parallel information-processing system. It has the capability to organize its components and to perform certain computations with a higher quality and many times faster than the fastest computer in existence today. Examples of these processes are pattern recognition, perception, and motor control. ANNs have been studied for more than four decades since Rosenblatt first applied the single-layer perceptrons to pattern-classification learning in the late 1950s.

An ANN is an abstract computational model of the human brain. The human brain has an estimated 1011 tiny units called neurons. These neurons are interconnected with an estimated 1015 links. Similar to the brain, an ANN is composed of artificial neurons (or processing units) and interconnections. When we view such a network as a graph, neurons can be represented as nodes (or vertices) and interconnections as edges. Although the term ANN is most commonly used, other names include “neural network,” parallel distributed processing (PDP) system, connectionist model, and distributed adaptive system. ANNs are also referred to in the literature as neurocomputers.

A neural network, as the name indicates, is a network structure consisting of a number of nodes connected through directional links. Each node represents a processing unit, and the links between nodes specify the causal relationship between connected nodes. All nodes are adaptive, which means that the outputs of these nodes depend on modifiable parameters pertaining to these nodes. Although there are several definitions and several approaches to the ANN concept, we may accept the following definition, which views the ANN as a formalized adaptive machine:

An ANN is a massive parallel distributed processor made up of simple processing units. It has the ability to learn experiential knowledge expressed through interunit connection strengths, and can make such knowledge available for use.

It is apparent that an ANN derives its computing power through, first, its massive parallel distributed structure and, second, its ability to learn and therefore to generalize. Generalization refers to the ANN producing reasonable outputs for new inputs not encountered during a learning process. The use of ANNs offers several useful properties and capabilities:

1. Nonlinearity. An artificial neuron as a basic unit can be a linear-or nonlinear-processing element, but the entire ANN is highly nonlinear. It is a special kind of nonlinearity in the sense that it is distributed throughout the network. This characteristic is especially important, for ANN models the inherently nonlinear real-world mechanisms responsible for generating data for learning.

2. Learning from Examples. An ANN modifies its interconnection weights by applying a set of training or learning samples. The final effects of a learning process are tuned parameters of a network (the parameters are distributed through the main components of the established model), and they represent implicitly stored knowledge for the problem at hand.

3. Adaptivity. An ANN has a built-in capability to adapt its interconnection weights to changes in the surrounding environment. In particular, an ANN trained to operate in a specific environment can be easily retrained to deal with changes