Core Concepts in Data Analysis: Summarization, Correlation by Boris Mirkin

By Boris Mirkin

Center ideas in facts research: Summarization, Correlation and Visualization presents in-depth descriptions of these facts research ways that both summarize facts (principal part research and clustering, together with hierarchical and community clustering) or correlate various facets of knowledge (decision bushes, linear principles, neuron networks, and Bayes rule).

Boris Mirkin takes an unconventional technique and introduces the concept that of multivariate facts summarization as a counterpart to traditional desktop studying prediction schemes, using strategies from data, facts research, facts mining, computing device studying, computational intelligence, and knowledge retrieval.

Innovations following from his in-depth research of the versions underlying summarization options are brought, and utilized to difficult matters reminiscent of the variety of clusters, combined scale facts standardization, interpretation of the suggestions, in addition to relatives among probably unrelated techniques: goodness-of-fit features for type timber and information standardization, spectral clustering and additive clustering, correlation and visualization of contingency info.

The mathematical element is encapsulated within the so-called “formulation” components, while so much fabric is brought via “presentation” elements that specify the equipment by way of using them to small real-world information units; concise “computation” components tell of the algorithmic and coding issues.

Four layers of energetic studying and self-study routines are supplied: labored examples, case stories, initiatives and questions.

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Additional resources for Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Undergraduate Topics in Computer Science)

Example text

Of the hundred entities in the set, the first 23 are classified as attacking the apache2 server, the 24–69 packets are normal, eleven entities 80–90 are consistent with a SAINT probe, and the last ten, 91–100, appear to be smurf attacks. These are examples of problems arising in relation to the Intrusion data: – identify features to judge whether the system functions normally or is it under attack (Correlation); – is there any relation between the protocol and type of attack (Correlation); – how to visualize the data reflecting similarity of the patterns (Summarization).

2 Highlighting To visually highlight a feature of an image one may distort the original dimensions. A good example is the London tube scheme by H. Beck (1906) which greatly enlarges relative sizes of the Centre of London part to make them better seen. Such a gross distortion, for a long while being totally rejected by the authorities, is now a standard for metro maps worldwide (see Fig. 3). In fact, this line of thinking has been worked on in geography for centuries, since the mapping of the Earth global surface to a flat sheet is impossible to do exactly.

2) and their descriptions in terms of combinations of edges of the rectangle with which they are drawn. A description may combine both edges present and absent to distinctively characterize a pattern, whereas a profile comprises edges that are present in all elements of its pattern. 3 and Mirkin 2005. ) Product C ECom Fig. 1 No Product A Patterns Fig. 12 Confusion patterns for numerals visualized from the patterns’ data analysis descriptions in terms of edges being present or not. 4 Narrating a Story In a situation in which data features involve a temporal and/or spatial aspects, integrating them in one image may lead to a visual narrative of a story, with its starting and ending dates, all on the same screen.

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