By Florin Gorunescu
The data discovery method is as outdated as Homo sapiens. till it slow in the past this procedure was once completely according to the ‘natural personal' machine supplied by way of mom Nature. thankfully, in contemporary many years the matter has all started to be solved according to the improvement of the information mining know-how, aided by way of the massive computational energy of the 'artificial' desktops. Digging intelligently in several huge databases, information mining goals to extract implicit, formerly unknown and probably necessary info from info, considering that “knowledge is power”. The objective of this publication is to supply, in a pleasant method, either theoretical suggestions and, particularly, functional concepts of this interesting box, able to be utilized in real-world occasions. consequently, it's intended for all those that desire to how to discover and research of enormous amounts of knowledge that allows you to realize the hidden nugget of data.
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Additional info for Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, Volume 12)
F rst consider the general model, sometimes called the saturated model and, by simplif cation, taking into account the specif c context, it arrives to the sought model. Despite many computing diffic lties, generally related to the model complexity, if it is well chosen, the proposed model will certainly be appropriate to the given situation. On the other hand, one can go in reverse order to identify the model within a class of models, namely in a ‘bottom-up’ manner. Taking into account the principle of simplicity, one starts with the simplest version, one that emphasizes the basic characteristic of the examined phenomenon, and as the need requires, one begins to increase the level of complexity.
In this respect, particularly for those with limited experience in the f eld or even novices, a special attention to the nature of the data must be paid, because there are dedicated programs which have no included warnings about the nature of input data, and thus the results are compromised. Knowledge about model parameters. Knowing the intimate nature of the modeled phenomenon, assumptions on the parameters of the proposed model can be made. , simple linear regression), and they have the same evolutionary trend, then we choose the parameter b > 0 from the beginning.
The choice of variables which are indeed essential for the model is the most important and sensitive issue at the same time, since the neglect of important variables is more “dangerous” than the inclusion of one which is not important. At this stage of modeling, the researcher should draw upon the expert in the specifi domain, to support him (her) to establish clearly the constituent variables and their appropriate hierarchy. It would be ideal to work ‘in team’ in the modeling process, to have the opportunity to choose the optimal variant at any time.