By Nong Ye

New applied sciences have enabled us to gather sizeable quantities of information in lots of fields. notwithstanding, our velocity of gaining knowledge of precious details and information from those info falls a long way in the back of our velocity of accumulating the information. **Data Mining: Theories, Algorithms, and Examples** introduces and explains a finished set of information mining algorithms from numerous information mining fields. The booklet reports theoretical rationales and procedural information of information mining algorithms, together with these as a rule present in the literature and people providing massive hassle, utilizing small info examples to give an explanation for and stroll throughout the algorithms.

The booklet covers a variety of facts mining algorithms, together with these mostly present in information mining literature and people now not absolutely coated in so much of present literature because of their substantial hassle. The e-book offers a listing of software program applications that help the knowledge mining algorithms, functions of the knowledge mining algorithms with references, and routines, besides the strategies guide and PowerPoint slides of lectures.

The writer takes a realistic method of information mining algorithms in order that the information styles produced may be totally interpreted. This process allows scholars to appreciate theoretical and operational facets of information mining algorithms and to manually execute the algorithms for a radical knowing of the knowledge styles produced by means of them.

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**Additional resources for Data Mining : Theories, Algorithms, and Examples**

**Sample text**

The following is another example of a linear regression model that is linear in the parameters: yi = β0 + β1xi ,1 + β 2 xi , 2 + β 3 log xi ,1xi , 2 + ε i . 2 Least-Squares Method and Maximum Likelihood Method of Parameter Estimation To fit a linear regression model to a set of training data (xi, yi), xi = (xi,1, …, xi,p), i = 1, …, n, the parameters βs need to be estimated. The least-squares method and the maximum likelihood method are usually used to estimate the parameters βs. 1. The least-squares method looks for the values of the parameters β0 and β1 that minimize the sum of squared errors (SSE) between observed target values (yi, i = 1, …, n) and the estimated target values (yˆi, i = 1, …, n) using the estimated parameters βˆ 0 and βˆ 1.

A variety of sequential and temporal patterns can be discovered using the data mining algorithms covered in Part VI of the book, including • Autocorrelation and time series analysis in Chapter 18 • Markov chain models and hidden Markov models in Chapter 19 • Wavelet analysis in Chapter 20 Chapters 10, 11, and 16 in Secure Computer and Network Systems: Modeling, Analysis and Design (Ye, 2008) give applications of sequential and temporal pattern mining algorithms to computer and network data for cyber attack detection.

12) The estimation of the parameters in the simple linear regression model based on the least-squares method does not require that the random error εi has a specific form of the probability distribution. 1 an assumption that εi is normally distributed with the mean of zero and the constant, unknown variance of σ2, denoted by N(0, σ2), the maximum likelihood method can also be used to estimate the parameters in the simple linear regression model. 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 .