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By Florin Gorunescu

The wisdom discovery method is as outdated as Homo sapiens. until eventually your time in the past this procedure used to be completely in accordance with the ‘natural own' computing device supplied via mom Nature. thankfully, in contemporary a long time the matter has all started to be solved in keeping with the advance of the knowledge mining expertise, aided via the massive computational strength of the 'artificial' desktops. Digging intelligently in several huge databases, facts mining goals to extract implicit, formerly unknown and in all probability invaluable info from facts, when you consider that “knowledge is power”. The objective of this publication is to supply, in a pleasant method, either theoretical thoughts and, particularly, sensible suggestions of this fascinating box, able to be utilized in real-world events. as a result, it's intended for all those that desire to the best way to discover and research of enormous amounts of information with a view to detect the hidden nugget of information.

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4 Problems Solvable with Data Mining 23 Fig. 10 Elbow criterion illustration this fact - the graph of the percentage of variance explained by clusters and depending on the number of clusters. Technically, the percentage of variance explained is the ratio of the between-group variance to the total variance. It is easy to see that if the number of clusters is larger than three, the gained information insignificantly increased, the curve having an “elbow” in point 3, and thus we will choose three as the optimum number of clusters in this case.

S. concept (Keep It Simple Series) applied in this circumstance: “between two models, comparable in performance, the simplest one will be chosen, which is probably closest to the truth, and is more easily accepted by others”. , 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).

Thus, deliberately ignoring any a priori model, one wonders just what data want “to tell” us. One can easily observe that this is the situation, in principle, regarding the process of ‘mining’ the data. It is indeed very difficult to foresee any scheme by just “reading” the data; instead, more experience is needed in their processing, but together with the other method, the first rudiments of the desired model will not delay to appear. Finally, we clearly conclude that a proper identification of the model needs a “fine” combination of the two methods.

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