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Although data mining has made major advancements
since it was first introduced, algorithms capable of
handling data with complex characteristics, e.g.,
high dimensionality, complex graph structures, etc.,
are only making mainstreams in recent years. Creating
such algorithms for complex data presents various
challenges, many of which can be overcame by
understanding the data characteristics via spectral
decomposition. This book aims to study the spectral
characteristics of data so that they can be exploited
by specific applications to achieve better results.
The book will address how spectral information can be
integrated into the needs of different analytical
tasks and hence, shed some light on this exciting
field. The book would be useful to professionals and
researchers in data mining and information analysis,
or anyone else who are interested in utilizing matrix
computation techniques in complex data analysis.
since it was first introduced, algorithms capable of
handling data with complex characteristics, e.g.,
high dimensionality, complex graph structures, etc.,
are only making mainstreams in recent years. Creating
such algorithms for complex data presents various
challenges, many of which can be overcame by
understanding the data characteristics via spectral
decomposition. This book aims to study the spectral
characteristics of data so that they can be exploited
by specific applications to achieve better results.
The book will address how spectral information can be
integrated into the needs of different analytical
tasks and hence, shed some light on this exciting
field. The book would be useful to professionals and
researchers in data mining and information analysis,
or anyone else who are interested in utilizing matrix
computation techniques in complex data analysis.