Data Mining - A Knowledge Discovery Approach by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski,

By Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, Lukasz Andrzej Kurgan

This entire textbook on information mining information the original steps of the information discovery procedure that prescribes the series within which information mining tasks can be played, from challenge and information realizing via facts preprocessing to deployment of the consequences. this data discovery strategy is what distinguishes information Mining from different texts during this sector. The e-book presents a set of routines and contains hyperlinks to tutorial displays. additionally, it comprises appendices of appropriate mathematical fabric.

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Only rules of a certain maximal length are considered, and longer rules are never generated. Optimization of the algorithm is often achieved by using efficient data structures, such as bit vectors, hash tables, and binary search trees, to store and manipulate the data. Finally, parallelization aims to distribute the processing of the data by the algorithm into several processors that work in parallel and thus can speed up the computations. – Techniques that partition the data set. This outcome can be achieved by reducing the dimensionality of the input data set through reduction of the number of objects, features, number of values per feature, and sequential or parallel processing of data divided into subsets.

6. Text Databases Text databases include features (attributes) that contain word descriptions for objects. These features are not simple nominal but hold long sentences or paragraphs of text. Examples for the heart clinic would be reports from patient interviews, physician’s notes and recommendations, descriptions of how a given drug works for a given patient, etc. ); semistructured, where some words or parts of the sentence are annotated, such as descriptions of how a drug works, which may use special annotations for the drug’s name and the dose; and structured, where all the words are annotated, like a physician’s recommendation that may be a fixed form listing only specific drugs and doses.

In those cases a process called discretization (see Chapter 8) becomes a necessary preprocessing step to transform continuous features into discrete ones, and this step must be completed before the data mining step is performed. Objects (also known as records, examples, units, cases, individuals, data points) represent entities described by one or more features. The term multivariate data refers to situation in which an object is described by many features, while with univariate data a single feature describes an object.

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