By Nong Ye
New applied sciences have enabled us to assemble vast quantities of information in lots of fields. besides the fact that, our speed of getting to know valuable details and data from those info falls some distance in the back of our speed of gathering the knowledge. Data Mining: Theories, Algorithms, and Examples introduces and explains a accomplished set of knowledge mining algorithms from a number of facts mining fields. The booklet experiences theoretical rationales and procedural information of information mining algorithms, together with these quite often present in the literature and people featuring significant hassle, utilizing small info examples to provide an explanation for and stroll during the algorithms.
The ebook covers quite a lot of info mining algorithms, together with these usually present in information mining literature and people now not totally coated in such a lot of latest literature because of their significant trouble. The e-book offers a listing of software program programs that aid the knowledge mining algorithms, functions of the knowledge mining algorithms with references, and workouts, besides the suggestions handbook and PowerPoint slides of lectures.
The writer takes a realistic method of info mining algorithms in order that the information styles produced should be absolutely interpreted. This method allows scholars to appreciate theoretical and operational features of knowledge mining algorithms and to manually execute the algorithms for a radical knowing of the information styles produced through them.
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Pdf). 1 and evaluate the classification performance of the naïve Bayes classifier by computing what percentage of the date records in the data set are classified correctly by the naïve Bayes classifier. 2, consider the LeakCheck Pressure as a categorical attribute with three categorical values and the Number of O-rings with Stress as a categorical target variable with three categorical values. Build a naïve Bayes classifier to classify the Number of O-rings with Stress from the Leak-Check Pressure and evaluate the classification performance of the naïve Bayes classifier by computing what percentage of the date records in the data set are classified correctly by the naïve Bayes classifier.
14) and the density function of the normal probability distribution: 1 y i − E( y i ) σ − 1 e 2 2πσ f ( yi ) = 2 = 1 yi − β0 − β1 xi σ − 1 e 2 2πσ 2 . 15) Because yis are independent, the likelihood of observing y1, …, yn, L, is the product of individual densities f(yi)s and is the function of β0, β1, and σ2: L (β0 , β1 , σ ) = n ∏ (2πσ ) 1 e 2 12 i =1 1 yi − β0 − β1 xi − 2 σ 2 . 16 are the maximum likelihood estimators and can be obtained by differentiating this likelihood function with respect to β0, β1, and σ2 and setting these partial derivatives to zero.
The exponential regression model given next is an example of nonlinear regression models: yi = β0 + β1eβ2 xi + ε i . 27) The logistic regression model given next is another example of nonlinear regression models: yi = β0 + εi . 28) The least-squares method and the maximum likelihood method are used to estimate the parameters of a nonlinear regression model. 21 for a linear regression model, the equations for a nonlinear regression model generally do not have analytical solutions because a nonlinear regression model is nonlinear in the parameters.