Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets. We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

Theneedforintelligentsystemstechnologyinsolvingreal-lifeproblemshasbeen consistently growing. In order to address this need, researchers in the ?eld have been developing methodologies and tools to develop intelligent systems for so- ing complex problems. The International Society of Applied Intelligence (ISAI) through its annual IEA/AIE conferences provides a forum for international s- enti?c and industrial community in the ?eld of Applied Arti?cial Intelligence to interactively participate in developing intelligent systems, which are needed to solve twenty ?rst century's ever growing problems in almost every ?eld. The 23rdInternationalConference on Industrial, Engineering and Other - plications of Applied Intelligence Systems (IEA/AIE-2010) held in C ordoba, Spain, followed IEA/AIE tradition of providing an international scienti?c forum for researchers in the ?eld of applied arti?cial intelligence. The presentations of theinvitedspeakersandauthorsmainlyfocusedondevelopingandstudyingnew methods to cope with the problems posed by real-life applications of arti?cial intelligence.Paperspresentedinthetwentythirdconferenceintheseriescovered theories as well as applications of intelligent systems in solving complex real-life problems. We received 297 papers for the main track, selecting 119 of them with the highest quality standards. Each paper was revised by at least three members of the Program Committee. The papers in the proceedings cover a wide number of topics including: applications to robotics, business and ?nancial markets, bio- formaticsandbiomedicine, applicationsofagent-basedsystems, computervision, control, simulation and modeling, data mining, decision support systems, evo- tionary computation and its applications, fuzzy systems and their applications, heuristic optimization methods and swarm intelligence, intelligent agent-based systems, internetapplications, knowledgemanagementandknowledgebaseds- tems, machine learning, neural network applications, optimization and heuristic search, and other real-life applications."

Theneedforintelligentsystemstechnologyinsolvingreal-lifeproblemshasbeen consistently growing. In order to address this need, researchers in the ?eld have been developing methodologies and tools to develop intelligent systems for so- ing complex problems. The International Society of Applied Intelligence (ISAI) through its annual IEA/AIE conferences provides a forum for international s- enti?c and industrial community in the ?eld of Applied Arti?cial Intelligence to interactively participate in developing intelligent systems, which are needed to solve twenty ?rst century's ever growing problems in almost every ?eld. The 23rdInternationalConference on Industrial, Engineering and Other - plications of Applied Intelligence Systems (IEA/AIE-2010) held in C' ordoba, Spain, followed IEA/AIE tradition of providing an international scienti?c forum for researchers in the ?eld of applied arti?cial intelligence. The presentations of theinvitedspeakersandauthorsmainlyfocusedondevelopingandstudyingnew methods to cope with the problems posed by real-life applications of arti?cial intelligence.Paperspresentedinthetwentythirdconferenceintheseriescovered theories as well as applications of intelligent systems in solving complex real-life problems. We received 297 papers for the main track, selecting 119 of them with the highest quality standards. Each paper was revised by at least three members of the Program Committee. The papers in the proceedings cover a wide number of topics including: applications to robotics, business and ?nancial markets, bio- formaticsandbiomedicine,applicationsofagent-basedsystems,computervision, control, simulation and modeling, data mining, decision support systems, evo- tionary computation and its applications, fuzzy systems and their applications, heuristic optimization methods and swarm intelligence, intelligent agent-based systems,internetapplications,knowledgemanagementandknowledgebaseds- tems, machine learning, neural network applications, optimization and heuristic search, and other real-life applications.

Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.

We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.

We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

IDEAL 2008 was the ninth IDEAL conference to take place; earlier editions were held in Hong Kong, the UK, Australia and Spain. This was the first time, though hopefully not the last time, that it took place in Daejeon, South Korea, during November 2-5, 2008. As the name suggests, the conference attracts researchers who are involved in either data engineering or learning or, increasingly, both. The former topic involves such aspects as data mining (or intelligent knowledge discovery from databases), infor- tion retrieval systems, data warehousing, speech/image/video processing, and mul- media data analysis. There has been a traditional strand of data engineering at IDEAL conferences which has been based on financial data management such as fraud det- tion, portfolio analysis, prediction and so on. This has more recently been joined by a strand devoted to bioinformatics, particularly neuroinformatics and gene expression analysis. Learning is the other major topic for these conferences and this is addressed by - searchers in artificial neural networks, machine learning, evolutionary algorithms, artificial immune systems, ant algorithms, probabilistic modelling, fuzzy systems and agent modelling. The core of all these algorithms is adaptation.

This book is the outcome of a decade's research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was "Negative Feedback as an Organising Principle for Arti?cial Neural Networks". Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from * Dr. Darryl Charles [24] in Chapter 5. * Dr. Stephen McGlinchey [127] in Chapter 7. * Dr. Donald MacDonald [121] in Chapters 6 and 8. * Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.