Data Mining: Concepts, Models and Techniques by Florin Gorunescu

By Florin Gorunescu

The wisdom discovery method is as outdated as Homo sapiens. until eventually a while in the past this procedure was once exclusively in line with the ‘natural own' laptop supplied through mom Nature. thankfully, in contemporary a long time the matter has started to be solved according to the advance of the information mining expertise, aided by way of the large computational strength of the 'artificial' pcs. Digging intelligently in numerous huge databases, information mining goals to extract implicit, formerly unknown and possibly worthwhile details from information, seeing that “knowledge is power”. The target of this booklet is to supply, in a pleasant approach, either theoretical techniques and, specifically, functional strategies of this intriguing box, able to be utilized in real-world events. therefore, it really is intended for all those that desire to the way to discover and research of huge amounts of information so one can realize the hidden nugget of information.

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See pictures below (Fig. 6), NASA, ESA, and The Hubble Heritage Team (STScI/AURA)). 2 Cluster Analysis By clustering we mean the method to divide a set of data (records/tuples/ vectors/instances/objects/sample) into several groups (clusters), based on certain 20 1 Introduction to Data Mining Fig. 6 Galaxy types predetermined similarities. Let us remember that the idea of partitioning a set of objects into distinct groups, based on their similarity, first appeared in Aristotle and Theophrastus (about fourth century BC), but the scientific methodology and the term ‘cluster analysis’ appeared for the first time, it seems, in <

As it was well observed, there is a very wide field both concerning the models/techniques considered in the study and the problems that can be solved with the data mining methodology. What is exciting to data mining is just this opening, quite rare in other areas of research, in terms of both area of applicability and field of used techniques. 6 Data Mining Applications So far we tried to describe, for each method separately, various successful applications of the data mining techniques in real-life situations.

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. Generally speaking, when a model is built to be used by laymen, for instance a machine learning model concerning the medical diagnosis used by doctors not trained in computer-aided diagnosis, it must have, as far as possible, a practical interpretation clearly presented to be understood by the user.

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