Knowledge Discovery Practices and Emerging Applications of by A.V. Senthil Kumar

By A.V. Senthil Kumar

Recent advancements have greatly elevated the quantity and complexity of information on hand to be mined, top researchers to discover new how one can glean non-trivial facts automatically.

Knowledge Discovery Practices and rising functions of information Mining: tendencies and New Domains introduces the reader to contemporary study actions within the box of information mining. This publication covers organization mining, class, cellular advertising, opinion mining, microarray information mining, net mining and purposes of information mining on organic info, telecommunication and allotted databases, between others, whereas selling realizing and implementation of knowledge mining suggestions in rising domains.

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Extra resources for Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains

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However, the comparison of results is another critical issue, because of the amount of different exploited experimental designs. In fact, the classification accuracy of an algorithm strongly depends on the exploited experimental design. 26 Decision Tree Decision trees are derived by using the simple divide-and-conquer algorithm. In these tree structures, leaves represent classes and branches represent conjunctions of features that lead to those classes. At each node of the tree, the attribute that most effectively splits samples into different classes is chosen.

For R example, if the disguise value in Age attribute is 0 and used frequently in the dataset, the recorded  + Sage =0 . A=v}. Frequently used notations used in this chapter are shown in Table 1. An example for a biased sample can be shown on a subset of a population satisfying certain criteria and/or constraints. For example, in a census data set, a subset of people who are under Table 1. A An entry in the recorded table Tv The projected database of value v Sv The disguised missing set of v Mv The maximal embedded unbiased sample of v f(T,T ') The correlation-based sample quality score 7 A Framework to Detect Disguised Missing Data 18 years old will be unmarried as it is illegal to get married before this age in some countries.

H0: The two samples come from a common distribution. Ha: The two samples do not come from a common distribution. Test Statistic: For the chi-square two-sample tests, the data is divided into k bins and the test statistic is defined as: (K R − K S )2   1 i 2 i  x = ∑    R + S  i =1  i i  2 k (9) where k is the number of categories (or bins), Ri is the observed frequency of bin i for the first sample, and Si is the observed frequency of bin 11 A Framework to Detect Disguised Missing Data Figure 4.

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