By Mehmed Kantardzic
This ebook experiences state of the art methodologies and strategies for reading hundreds and hundreds of uncooked information in high-dimensional facts areas, to extract new details for determination making. The goal of this booklet is to provide a unmarried introductory resource, prepared in a scientific approach, during which lets direct the readers in research of enormous facts units, in the course of the clarification of uncomplicated techniques, versions and methodologies built in contemporary many years.
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Additional resources for Data Mining. Concepts, Models, Methods, and Algorithms
If we have only a few hundred samples for analysis, dimensionality reduction is required in order for any reliable model to be mined or to be of any practical use. On the other hand, data overload, because of high dimensionality, can make some data-mining algorithms nonapplicable, and the only solution is again a reduction of data dimensions. The three main dimensions of preprocessed data sets, usually represented in the form of flat files, are columns (features), rows (cases or samples), and valves of the features.
Outlier detection and potential removal from a data set can be described as a process of the selection of k out of a samples that are considerably dissimilar, exceptional, or inconsistent with respect to the remaining data. The problem of defining outliers is nontrivial, especially in multidimensional samples. Data visualization methods that are useful in outlier detection for one to three dimensions are weaker in multidimensional data because of a lack of adequate visualization methodologies for these spaces.
2 where a two-dimensional space is transformed into a one-dimensional space in which the data set has the highest variance. In practice, it is not possible to determine matrix A directly, and therefore we compute the covariance matrix S as a first step in features transformation. Matrix S is defined as where . The eigenvalues of the covariance matrix S for the given data should be calculated in the next step. Finally, the m eigenvectors corresponding to the m largest eigenvalues of S define a linear transformation from the n-dimensional space to an m-dimensional space in which the features are uncorrelated.