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Books > Computing & IT > Applications of computing > Artificial intelligence
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Non-Standard Parameter Adaptation for Exploratory Data Analysis (Paperback, 2009 ed.)
Loot Price: R2,447
Discovery Miles 24 470
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Non-Standard Parameter Adaptation for Exploratory Data Analysis (Paperback, 2009 ed.)
Series: Studies in Computational Intelligence, 249
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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.
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