Clustering is one of the most fundamental and essential data
analysis techniques. Clustering can be used as an independent data
mining task to discern intrinsic characteristics of data, or as a
preprocessing step with the clustering results then used for
classification, correlation analysis, or anomaly detection.
Kogan and his co-editors have put together recent advances in
clustering large and high-dimension data. Their volume addresses
new topics and methods which are central to modern data analysis,
with particular emphasis on linear algebra tools, opimization
methods and statistical techniques. The contributions, written by
leading researchers from both academia and industry, cover
theoretical basics as well as application and evaluation of
algorithms, and thus provide an excellent state-of-the-art
overview.
The level of detail, the breadth of coverage, and the
comprehensive bibliography make this book a perfect fit for
researchers and graduate students in data mining and in many other
important related application areas.
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