New PDF release: Clustering for Data Mining: A Data Recovery Approach

By Boris Mirkin

frequently thought of extra as an paintings than a technology, the sector of clustering has been ruled by means of studying via examples and through concepts selected virtually via trial-and-error. Even the most well-liked clustering methods--K-Means for partitioning the knowledge set and Ward's procedure for hierarchical clustering--have lacked the theoretical recognition that might identify a company dating among the 2 equipment and proper interpretation aids.

Rather than the normal set of advert hoc options, Clustering for information Mining: a knowledge restoration process offers a conception that not just closes gaps in K-Means and Ward tools, but in addition extends them into parts of present curiosity, corresponding to clustering combined scale information and incomplete clustering. the writer indicates unique equipment for either cluster discovering and cluster description, addresses comparable subject matters comparable to important part research, contingency measures, and knowledge visualization, and contains approximately 60 computational examples overlaying all phases of clustering, from information pre-processing to cluster validation and effects interpretation.

This author's particular awareness to information restoration tools, theory-based recommendation, pre- and post-processing concerns which are past the scope of such a lot texts, and transparent, functional directions for real-world facts mining make this booklet perfect for nearly 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

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