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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