Florin Gorunescu's Data Mining: Concepts, Models and Techniques (Intelligent PDF

By Florin Gorunescu

ISBN-10: 3642197205

ISBN-13: 9783642197208

The information discovery procedure is as outdated as Homo sapiens. until eventually a while in the past this technique was once completely in accordance with the ‘natural personal' machine supplied by way of mom Nature. thankfully, in fresh a long time the matter has all started to be solved in accordance with the improvement of the knowledge mining expertise, aided by means of 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 likelihood precious details from facts, in view that “knowledge is power”. The aim of this publication is to supply, in a pleasant method, either theoretical thoughts and, particularly, sensible options of this intriguing box, able to be utilized in real-world occasions. consequently, it really is intended for all those that desire to methods to discover and research of huge amounts of information on the way to notice the hidden nugget of knowledge.

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Get Data Mining: Concepts, Models and Techniques (Intelligent PDF

The information discovery procedure is as outdated as Homo sapiens. till your time in the past this method used to be exclusively in response to the ‘natural personal' laptop supplied via mom Nature. thankfully, in fresh many years the matter has started to be solved in accordance with the advance of the information mining know-how, aided by means of the large computational energy of the 'artificial' desktops.

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

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Once the model f tted (adjusted) to data, it remains to be validated before its successful application to solve real-life problems. The notion of validating a given model covers a wide enough range of issues to be considered. Thus, by “validation” we understand the level of the practical value of the model in explaining a particular phenomenon, assessing both its similarity to the prototype and its effectiveness in a straight application to a given situation. We speak of validating the model: • in the model development, the adjusted model revealing new aspects concerning data; • in the testing phase, when new data are collected and used to optimize the proposed model; • in the practical application stage, when procedures for “monitoring” are introduced to check whether the originally proposed model is effective or not in “live” conditions.

Fig. , global warming effect) are among the most ‘hot’ topics in current research. org). nasa. gov/topics/earth/). , sequences of collections of items. A typical example of such data refers to data stored in a customer database, in which each transaction is a collection of goods purchased by a customer in a single visit to the supermarket (market basket data) -see Fig. 5. Another type of such transactional data refers to data concerning the Web usage. We illustrate below examples of such ordered data -see Fig.

In the f rst case, for instance, the idea is to equip these intervention vehicles with equipment and devices that are frequently used in different types of interventions, so that, when there is a new application for a particular intervention, the utility vehicle is properly equipped for intervention, saving time and fuel needed to ‘repair’ the lack of resource management. In this case, association rules are identif ed by processing the data referring to the type of devices and parts used in previous interventions in order to address various issues arising on the spot.

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Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, Volume 12) by Florin Gorunescu


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