Neural Networks in Data Mining, Exploratory Data Analysis

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Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data.

Neural Networks is one of the Data Mining Techniques. The first step is to design a specific network architecture (that includes a specific number of “layers” each consisting of a certain number of “neurons”).

The size and structure of the network needs to match the nature (e.g., the formal complexity) of the investigated phenomenon. Because the latter is obviously not known very well at this early stage, this task is not easy and often involves multiple “trials and errors.” (Now, there is, however, neural network software that applies artificial intelligence techniques to aid in that tedious task and finds “the best” network architecture) .

The new network is then subjected to the process of "training" .

In that phase, neurons apply an iterative process to the number of inputs (variables) to adjust the weights of the network in order to optimally predict (in traditional terms, we could say find a "fit" to) the sample data on which the "training" is performed.

After the phase of learning from an existing data set, the new network is ready and it can then be used to generate predictions. The resulting "network" developed in the process of "learning" represents a pattern detected in the data. Thus, in this approach, the "network" is the functional equivalent of a model of relations between variables in the traditional model building approach.

Image source : developers.google.com

However, unlike in the traditional models, in the "network," those relations cannot be articulated in the usual terms used in statistics or methodology to describe relations between variables (such as, for example, "A is positively correlated with B but only for observations where the value of C is low and D is high"). Some neural networks can produce highly accurate predictions; they represent, however, a typical a-theoretical (one can say, "a black box") research approach.

That approach is concerned only with practical considerations, that is, with the predictive validity of the solution and its applied relevance and not with the nature of the underlying mechanism or its relevance for any "theory" of the underlying phenomena.

However, it should be mentioned that Neural Network techniques can also be used as a component of analyses designed to build explanatory models because Neural Networks can help explore data sets in search for relevant variables or groups of variables; the results of such explorations can then facilitate the process of model building. Moreover, now there is neural network software that uses sophisticated algorithms to search for the most relevant input variables, thus potentially contributing directly to the model building process. One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter. 

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An important disadvantage, however, is that the final solution depends on the initial conditions of the network, and, as stated before, it is virtually impossible to "interpret" the solution in traditional, analytic terms, such as those used to build theories that explain phenomena.

Some authors stress the fact that neural networks use, or we should say are expected to use, massively parallel computation models. For example Haykin (1994) defines neural network as:

"a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects:

(1) Knowledge is acquired by the network through a learning process, and

(2) Interneuron connection strengths known as synaptic weights are used to store the knowledge."

However, as Ripley (1996) points out, the vast majority of contemporary neural network applications run on single-processor computers and he argues that a large speed-up can be achieved not only by developing software that will take advantage of multiprocessor hardware by also by designing better (more efficient) learning algorithms.

Neural networks is one of the methods used in Data Mining; see also Exploratory Data Analysis. For more information on neural networks, see Haykin (1994), Masters (1995), Ripley (1996), and Welstead (1994).

For a discussion of neural networks as statistical tools, see Warner and Misra (1996) .

Reference source : documentation(dot)statsoft(dot)com › STATISTICAHelp › NeuralNetworks