Often considered more of an art than a science, books on
clustering have been dominated by learning through example with
techniques chosen almost through trial and error. Even the two most
popular, and most related, clustering methods K-Means for
partitioning and Ward's method for hierarchical clustering have
lacked the theoretical underpinning required to establish a firm
relationship between the two methods and relevant interpretation
aids. Other approaches, such as spectral clustering or consensus
clustering, are considered absolutely unrelated to each other or to
the two above mentioned methods.
Clustering: A Data Recovery Approach, Second Edition presents a
unified modeling approach for the most popular clustering methods:
the K-Means and hierarchical techniques, especially for divisive
clustering. It significantly expands coverage of the mathematics of
data recovery, and includes a new chapter covering more recent
popular network clustering approaches spectral, modularity and
uniform, additive, and consensus treated within the same data
recovery approach. Another added chapter covers cluster validation
and interpretation, including recent developments for
ontology-driven interpretation of clusters. Altogether, the
insertions added a hundred pages to the book, even in spite of the
fact that fragments unrelated to the main topics were removed.
Illustrated using a set of small real-world datasets and more
than a hundred examples, the book is oriented towards students,
practitioners, and theoreticians of cluster analysis. Covering
topics that are beyond the scope of most texts, the author s
explanations of data recovery methods, theory-based advice, pre-
and post-processing issues and his clear, practical instructions
for real-world data mining make this book ideally suited for
teaching, self-study, and professional reference.
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