What is data mining?

Simply stated,data mining refers to extracting or “mining” knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, “data mining” should have been more appropriately named “knowledge mining from data”, which is unfortunately somewhat long. “Knowledge mining”, a shorter term, may not reect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that nds a smallset of precious nuggets from a great deal of raw material (Figure 1.3). Thus, such a misnomer which carries both”data” and “mining” became a popular choice. There are many other terms carrying a similar or slightly dierent meaning to data mining, such as knowledge mining from databases, knowledge extraction, data/pattern analysis, data archaeology, and data dredging .
Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases “, or KDD . Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery as a process is depicted in Figure 1.4, and consists of an iterative sequence of the following steps:

data cleaning (to remove noise or irrelevant data),

data integration (where multiple data sources may be combined)

data selection (where data relevant to the analysis task are retrieved from the database),

data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

data mining(an essential process where intelligent methods are applied in order to extract data patterns),

pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures; Section 1.5),
and

knowledge presentation (where visualization and knowledge representation techniques are used to present

Ant Colony Optimization

Ant Colony Optimization

Marco Dorigo, Thomas Stützle, “Ant Colony Optimization (Bradford Books)” The MIT Press

2004 ISBN: 0262042193 319 pages PDF 1,9 MB

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The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.
The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
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An Interface Layer for Artificial Intelligence

Markov Logic: An Interface Layer for Artificial Intelligence
Pedro Domingos, Daniel Lowd, “Markov Logic: An Interface Layer for Artificial Intelligence”

Morgan & Claypool 2009 ISBN: 1598296922 100 pages PDF 1,1 MB
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An Introduction to Artificial Intelligence

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can “think”.
In order to classify machines as “thinking”, it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception have aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the human brain, made up of billions of neurons, and arguably the most complex matter in the universe. Perhaps the best way to gauge the intelligence of a machine is British computer scientist Alan Turing’s test. He stated that a computer would deserves to be called intelligent if it could deceive a human into believing that it was human.
Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. Over the next four decades, despite many stumbling blocks, AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.
AI has always been on the pioneering end of computer science. Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence. The theory and insights brought about by AI research will set the trend in the future of computing. The products available today are only bits and pieces of what are soon to follow, but they are a movement towards the future of artificial intelligence. The advancements in the quest for artificial intelligence have, and will continue to affect our jobs, our education, and our lives.

The Fuzzy Systems Handbook

The Fuzzy Systems Handbook
A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems

Earl CoxAcademic Press 1994 ISBN: 0121942708 512 pages PDF 6,7 MB

A comprehensive introduction to fuzzy logic, this book leads the reader through the complete process of designing, constructing, implementing, verifying and maintaining a platform-independent fuzzy system model. It is written in a tutorial style that assumes no background in fuzzy logic on the reader’s part. The enclosed disk contains all of the book’s examples in C++ code.
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The First Post In This Site

Hello my friends.
I decided to gather all my activities in this site.

I started many activities in this domain.

I expand my interesting project in AI and ES.

GOD BYE..