Data Mining in Agriculture (Springer Optimization and Its by Panos M. Pardalos, Antonio Mucherino, Petraq J. Papajorgji

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 facts mining innovations utilized to agriculture and environmental similar fields. This booklet provides either theoretical and functional insights with a spotlight on proposing the context of every info mining approach quite intuitively with abundant concrete examples represented graphically and with algorithms written in MATLAB®.

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Extra info for Data Mining in Agriculture (Springer Optimization and Its Applications)

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Harmony search [82] is inspired by jazz music improvisation, and it seeks the optimal value of an optimization problem the same way musicians look for perfect harmonies. Many meta-heuristic methods took inspiration from animal behavior. Swarm intelligence can be defined as the collective intelligence that emerges from a group of simple entities, such as ant colonies, flocks of birds, termites, swarm of bees, and schools of fish [148]. Ant colony optimization [64] algorithms simulate the behavior of a colony of ants finding and conserving food supplies, whereas particle swarm optimization [126] simulates the motion of a large number of insects or other organisms.

Let us now define a1 as follows: a1 = y2 − y1 . 5) passes through both (x1 , y1 ) and (x2 , y2 ). Indeed, if x = x2 , it follows that y = a0 + a1 (x2 − x1 ) = y1 + y2 − y1 (x2 − x1 ) = y2 , x2 − x1 and then the line passes through (x2 , y2 ) as well. Supposing that there is a third point (x3 , y3 ) in the training set, then a straight line is not sufficient anymore, unless the three points are aligned. The following polynomial having degree 2 can be used y = a0 + a1 (x − x1 ) + a2 (x − x1 )(x − x2 ) for modeling the set of data.

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. In this case, there is only one complex variable, the network, and its nodes have to be partitioned. Similar nodes can be grouped in the same cluster, which defines a sort of sub-network of the original one. In such sub-networks, nodes are connected to each other, because they are similar. These kinds of networks are called cliques in graph theory.

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