By Florin Gorunescu
The information discovery strategy is as outdated as Homo sapiens. until eventually a while in the past this strategy used to be exclusively according to the ‘natural personal' laptop supplied through mom Nature. thankfully, in contemporary a long time the matter has started to be solved according to the advance of the knowledge mining know-how, aided through the massive computational strength of the 'artificial' pcs. Digging intelligently in several huge databases, info mining goals to extract implicit, formerly unknown and in all likelihood necessary info from info, considering the fact that “knowledge is power”. The target of this publication is to supply, in a pleasant method, either theoretical ideas and, specifically, useful options of this interesting box, able to be utilized in real-world events. hence, it really is intended for all those that desire to tips on how to discover and research of huge amounts of information with the intention to realize the hidden nugget of knowledge.
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Extra info for Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, Volume 12)
Outliers can occur by chance in any dataset, but they are often generated by measurement errors. Usually, outliers are either discarded from the dataset, or methods that are robust to them are used. ) might be detected by specifi data mining methods. Fig. 7 illustrates such data. In cases where there is no value available for some attributes of objects in the database, we speak of missing values. Frequently, values are missing essentially at random. For instance, in health care, some clinical records may have been not recorded, or may have been destroyed by error, or lost.
P. but some are useful” and “Statisticians, like artists, have the bad habit of falling in love with their models”, true for any scientist, indeed. ); • Building the model and testing/validating it (select the best model based on its predictive performance - assessing the competitiveness of models); • Applying the model in practice, evaluating thus its effectiveness. Regarding the implementation of various data mining models, we present below a list of various software systems based on them. ) In the previous section we mentioned some of the most popular models of data mining and some applications related to them.
So, the process itself of f nding the models is not too important here, although this action has its importance and a special charm for connoisseurs, but f nding ‘natural’ models, that match as close as possible with a given prototype. , the models being applied in various concrete situations. Iteration. 5 About Modeling and Models 29 in the strict order of the points in the program. , all having the chance of a more “liberal” program in the conception process. ” Modeling involves frequent returns to previous stages, changes in the model design, discovery of issues that were initially ignored, but which are essential in a deeper thinking, etc.