By Boris Mirkin
Rather than the normal set of advert hoc innovations, Clustering for information Mining: an information restoration technique provides a idea that not just closes gaps in K-Means and Ward tools, but additionally extends them into parts of present curiosity, resembling clustering combined scale facts and incomplete clustering. the writer indicates unique equipment for either cluster discovering and cluster description, addresses similar subject matters akin to primary part research, contingency measures, and knowledge visualization, and contains approximately 60 computational examples masking all phases of clustering, from info pre-processing to cluster validation and effects interpretation.
This author's special recognition to information restoration tools, theory-based recommendation, pre- and post-processing matters which are past the scope of so much texts, and transparent, functional directions for real-world facts mining make this publication superb for almost all reasons: for instructing, for self-study, and for pro reference.
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