By Panos M. Pardalos, Antonio Mucherino, Petraq J. Papajorgji
Data Mining in Agriculture represents a finished attempt to supply graduate scholars and researchers with an analytical textual content on info mining strategies utilized to agriculture and environmental similar fields. This publication offers either theoretical and functional insights with a spotlight on proposing the context of every information mining process really intuitively with abundant concrete examples represented graphically and with algorithms written in MATLAB®.
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Data Mining in Agriculture (Springer Optimization and Its Applications)
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Extra resources for Data Mining in Agriculture (Springer Optimization and Its Applications)
Example text
Such network can be studied with the purpose of revealing the trends that can take place in the stock market. Given a certain set of marketing data, a network can be associated to it. In the network, stocks having similar behaviors are connected by links. Grouping together stocks with similar market properties is useful for studying the market trends. Clustering techniques can be used for this purpose. However, in this case, the problem is different from the usual. 1 introduces clustering techniques as techniques for grouping data in different clusters.
If particular properties about the model are not known, but high oscillations must be avoided, then a spline function can be used, instead of a polynomial. A spline is a function defined piecewise by polynomials. It is used for avoiding the phenomenon of the increase of oscillations when the degree of a polynomial increases. Indeed, a spline locally is a polynomial having a low degree, so that its oscillations are low. In its general form a polynomial spline S : [a, b] −→ consists of polynomial pieces Pi : [ti , ti+1 ) ∈ [a, b] −→ ∀i ∈ {1, 2, .
3 Applications 37 PCA can be used as a data mining technique itself, but more often it is used for reducing the dimension of a set of data before applying some other data mining technique. 1. Moreover, PCA is also used in some of the applications discussed in other chapters of this book, which are devoted to other data mining techniques. 1, PCA is used in conjunction with the k-means algorithm for data partitioning. In this application, the wine fermentation process is studied and the aim is to find clues that reveal bad results at the beginning of the fermentation process [230].