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

February 2, 2018 | Data Mining | By admin | 0 Comments

By Florin Gorunescu

ISBN-10: 3642197205

ISBN-13: 9783642197208

The information discovery method is as outdated as Homo sapiens. until eventually it slow in the past this procedure was once exclusively in response to the ‘natural personal' desktop supplied via mom Nature. thankfully, in fresh many years the matter has began to be solved in accordance with the advance of the knowledge mining know-how, aided by way of the large computational strength of the 'artificial' pcs. Digging intelligently in numerous huge databases, facts mining goals to extract implicit, formerly unknown and very likely important info from info, considering “knowledge is power”. The target of this publication is to supply, in a pleasant means, either theoretical techniques and, particularly, functional suggestions of this fascinating box, able to be utilized in real-world occasions. consequently, it truly is intended for all those that desire to methods to discover and research of huge amounts of information so one can become aware of the hidden nugget of knowledge.

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

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First, it is necessary that the chosen model is suitable for the volume of available data. , weather forecast models, which require a sufficien large number of data in order that the theoretical model can be adjusted to data (f tting model to data). Secondly, it is about the separate analysis of data (disaggregation), or their simultaneous analysis (aggregation), this fact depending on each case, usually working simultaneously with the two types of analysis. Thirdly, we should consider reliable data only, otherwise the proposed model has no practical value.

Fig. com/white+noise). Fig. 6 Example of white noise in data Basically, an outlier is an object (observation) that is, in a certain way, distant from the rest of the data. In other words, it represents an ‘alien’ object in the dataset, with characteristics considerably different to most of the other objects in the dataset. ). 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.

First, it is necessary that the chosen model is suitable for the volume of available data. , weather forecast models, which require a sufficien large number of data in order that the theoretical model can be adjusted to data (f tting model to data). Secondly, it is about the separate analysis of data (disaggregation), or their simultaneous analysis (aggregation), this fact depending on each case, usually working simultaneously with the two types of analysis. Thirdly, we should consider reliable data only, otherwise the proposed model has no practical value.

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


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