TY - BOOK ID - 8060521 TI - Non-standard parameter adaptation for exploratory data analysis AU - Barbakh, Wesam Ashour. AU - Fyfe, Colin. AU - Wu, Ying. PY - 2009 SN - 3642040047 3642040055 PB - Berlin : Springer, DB - UniCat KW - Cluster analysis KW - Machine learning KW - Artificial intelligence KW - Mathematical Statistics KW - Civil Engineering KW - Applied Mathematics KW - Civil & Environmental Engineering KW - Engineering & Applied Sciences KW - Mathematics KW - Physical Sciences & Mathematics KW - Data processing KW - Methodology KW - Cluster analysis. KW - Cross-entropy method. KW - CE method KW - Computer science. KW - Data mining. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Computer Science. KW - Data Mining and Knowledge Discovery. KW - Artificial Intelligence (incl. Robotics). KW - Appl.Mathematics/Computational Methods of Engineering. KW - Estimation theory KW - Mathematical optimization KW - Correlation (Statistics) KW - Multivariate analysis KW - Spatial analysis (Statistics) KW - Artificial Intelligence. KW - Mathematical and Computational Engineering. KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching UR - https://www.unicat.be/uniCat?func=search&query=sysid:8060521 AB - 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. ER -