By Boris Mirkin
Middle ideas in information research: Summarization, Correlation and Visualization presents in-depth descriptions of these facts research techniques that both summarize info (principal part research and clustering, together with hierarchical and community clustering) or correlate assorted features of knowledge (decision bushes, linear ideas, neuron networks, and Bayes rule).
Boris Mirkin takes an unconventional strategy and introduces the concept that of multivariate information summarization as a counterpart to standard laptop studying prediction schemes, using thoughts from information, info research, facts mining, desktop studying, computational intelligence, and knowledge retrieval.
Innovations following from his in-depth research of the versions underlying summarization thoughts are brought, and utilized to tough concerns akin to the variety of clusters, combined scale facts standardization, interpretation of the ideas, in addition to family among doubtless unrelated techniques: goodness-of-fit services for type timber and information standardization, spectral clustering and additive clustering, correlation and visualization of contingency facts.
The mathematical element is encapsulated within the so-called “formulation” components, while so much fabric is brought via “presentation” elements that specify the tools through utilizing them to small real-world info units; concise “computation” elements tell of the algorithmic and coding issues.
Four layers of energetic studying and self-study routines are supplied: labored examples, case reviews, tasks and questions.
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Additional info for Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Undergraduate Topics in Computer Science)
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