By Boris Mirkin
Rather than the normal set of advert hoc recommendations, Clustering for facts Mining: a knowledge restoration method offers a thought that not just closes gaps in K-Means and Ward equipment, but in addition extends them into parts of present curiosity, resembling clustering combined scale information and incomplete clustering. the writer indicates unique equipment for either cluster discovering and cluster description, addresses comparable themes resembling relevant part research, contingency measures, and information visualization, and contains approximately 60 computational examples masking all phases of clustering, from facts pre-processing to cluster validation and effects interpretation.
This author's specific awareness to facts restoration tools, theory-based recommendation, pre- and post-processing matters which are past the scope of such a lot texts, and transparent, useful directions for real-world facts mining make this booklet supreme for almost all reasons: for educating, for self-study, and for pro reference.
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Clustering for Data Mining: A Data Recovery Approach (Chapman & Hall/CRC Computer Science & Data Analysis) by Boris Mirkin