By Florin Gorunescu
The wisdom discovery strategy is as outdated as Homo sapiens. until eventually it slow in the past this technique used to be exclusively in keeping with the ‘natural own' machine supplied by means of mom Nature. thankfully, in fresh many years the matter has started to be solved in line with the advance of the information mining expertise, aided by means of the large computational energy of the 'artificial' pcs. Digging intelligently in numerous huge databases, info mining goals to extract implicit, formerly unknown and probably worthy info from information, on account that “knowledge is power”. The target of this publication is to supply, in a pleasant approach, either theoretical thoughts and, particularly, sensible thoughts of this intriguing box, able to be utilized in real-world occasions. therefore, it truly is intended for all those that desire to easy methods to discover and research of huge amounts of knowledge so that it will realize the hidden nugget of information.
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Extra resources for Data Mining: Concepts, Models and Techniques
In Statistics, regression analysis means the mathematical model which establishes (concretely, by the regression equation) the connection between the values of a given variable (response/outcome/dependent variable) and the values of other variables (predictor/independent variables). The best known example of regression is perhaps the identification of the relationship between a person’s height and weight, displayed in tables obtained by using the regression equation, thereby evaluating an ideal weight for a specified height.
To the real problem). It remains now to adjust the model to observed data. We will mention three criteria, known as adjustment (fitness) criteria, underlying the assessment of ‘fitting the model to data’, and based on which we will consider different methods of adjustment (fitting). • Establishing the equality between the characteristics of the model form and the characteristics of the data form; • Measuring the deviation between model and data; • Establishing the extent on which the model is justified by the data.
To solve this problem the elbow criterion is usually used (Fig. 10). It basically says that we should choose a number of clusters so that adding another cluster does not add sufficient information to continue the process. Practically, the analysis of variance is used to ‘measure’ how well the data segmentation has been performed in order to obtain a small intra-cluster variability/variance and a large inter-cluster variability/variance, according to the number of chosen clusters. 4 Problems Solvable with Data Mining 23 Fig.