Data Mining Chapter 2 Pdf Chapter 2 data mining free download as pdf file (.pdf), text file (.txt) or view presentation slides online. the document discusses different techniques for measuring similarity and dissimilarity between data objects. Course lecture is very heavily based on “introduction to data mining” by tan, steinbach, karpatne, kumar.
Data Mining Pdf Data mining 2.1 introduction eresting to the user. it is an interdisciplinary field and a very broad and active research area, with many annual international conferences and sev eral academic journals entirely devoted to the field. therefore, it is impossible to review the entire field i. Numerical measure of how alike two data objects are. is higher when objects are more alike. the following table shows the similarity and dissimilarity between two objects, x and y, with respect to a single, simple attribute. Loading…. Integers in some given data file: nominal, ordinal, or ratio scale? nominal attributes: histograms (distribution consistent with background knowledge?) numeric attributes: graphs (any obvious outliers?) too much data to inspect? take a sample!.
Data Mining Unit 2 Pdf Loading…. Integers in some given data file: nominal, ordinal, or ratio scale? nominal attributes: histograms (distribution consistent with background knowledge?) numeric attributes: graphs (any obvious outliers?) too much data to inspect? take a sample!. Good summaries of statistical descriptive data mining methods include freedman, pisani and purves [fpp97], and devore [dev95]. for statistics based visualization of data using boxplots, quantile plots, quantile quantile plots, scatter plots, and loess curves, see cleveland [cle93]. If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multi dimensional space, where each dimension represents a distinct attribute. Recursively reduce the data by collecting and replacing low level concepts (such as numeric values for age) by higher level concepts (such as young, middle aged, or senior). Scriptive data mining tasks. three classical point estimation methods — the method of moments, maximum likelihood estimation, and the expectation maximization algo rithm — are discussed in this chapter, followed by a review of measuremen.
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