Download Data Mining: Concepts, Models and Techniques (Intelligent by Florin Gorunescu PDF

By Florin Gorunescu

The information discovery method is as previous as Homo sapiens. until eventually a while in the past this strategy was once completely in accordance with the ‘natural personal' computing device supplied through mom Nature. thankfully, in contemporary many years the matter has all started to be solved in keeping with the advance of the knowledge mining know-how, aided through the large computational energy of the 'artificial' pcs. Digging intelligently in several huge databases, info mining goals to extract implicit, formerly unknown and most likely worthwhile details from facts, because “knowledge is power”. The aim of this booklet is to supply, in a pleasant manner, either theoretical innovations and, particularly, sensible concepts of this fascinating box, able to be utilized in real-world occasions. consequently, it really is intended for all those that desire to the best way to discover and research of huge amounts of knowledge so as to become aware of the hidden nugget of data.

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Read or Download Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, Volume 12) PDF

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Additional resources for Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, Volume 12)

Example text

A classic example of such a data collection is the hospital patient registry containing patient medical records. ). 1) a f ctitious example of such data is presented in short (we have deliberately ignored the f rst column of the registry containing the patient’s identity). 2 below. 3). ) -see Fig. 2 and Fig. 3 below. Fig. 2 Types of Datasets 49 Fig. 3 Example of a graph diagram (HTML link) Ordered datasets refer to those data collections which are, one way or another, governed by a specifi order in which objects appear.

Full Members: • • • • IBM Corp. , Chicago, IL B. , San Diego, CA C. Contributing Members: Equifax, Atlanta, GA; Fair Isaac, Minneapolis, MN; KNIME, Konstanz, Germany; NASA, KSC, FL; National Center for Data Mining, University of Illinois at Chicago; Open Data Group, River Forest, IL; Rapid-I, Dortmund, Germany; Togaware Pty Ltd, Canberra, Australia; Visa, San Francisco, CA. Also in this respect, we mention that, in recent years, international conferences with the main theme concerning the standardization of the data mining procedures took place.

7 Data Mining Terminology In the data mining area there are already fundamental concepts and a specif c terminology, even if the fiel is still not mature enough. As it will be seen below, many terms are previously known from other well-established research areas, but since those techniques were naturally borrowed by data mining, they were adopted without any complex. Thus, we could say that it is diff cult to speak of a genuine specif c data mining terminology. In conclusion, once borrowing “tools” from neighboring domains, their names were also borrowed.

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