Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner's Automated Data Collection with R: A Practical Guide to Web PDF

By Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner

A fingers on advisor to net scraping and textual content mining for either newbies and skilled clients of R Introduces basic options of the most structure of the net and databases and covers HTTP, HTML, XML, JSON, SQL.

Provides simple concepts to question net records and knowledge units (XPath and general expressions). an in depth set of workouts are provided to lead the reader via every one procedure.

Explores either supervised and unsupervised ideas in addition to complex innovations reminiscent of information scraping and textual content administration. Case stories are featured all through besides examples for every method offered. R code and options to routines featured within the e-book are supplied on a aiding site.

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The number and composition of clusters can be visually determined based on the output distribution generated by the training process. With only input variables in the training sample, SOM aims to learn or discover the underlying structure of the data. A typical SOM network has two layers of nodes, an input layer and output layer (sometimes called the Kohonen layer). Each node in the input layer is fully connected to nodes in the two-dimensional output layer. Figure 4 shows an example of an SOM network with several input nodes in the input layer and a two dimension output layer with a 4x4 rectangular array of 16 neurons.

Funahashi (1998) shows that for the two-group d-dimensional Gaussian classification problem, neural networks with at least 2d hidden nodes have the capability to approximate the posterior probability with arbitrary accuracy when infinite data is available and the training proceeds ideally. Miyake and Kanaya (1991) shows that neural networks trained with a generalized mean-squared error objective function can yield the optimal Bayes rule. 30 G. Peter Zhang As the statistical counterpart of neural networks, discriminant analysis is a well-known supervised classifier.

As the number of cycles of training (epochs) increases, better formation of the clusters can be found. Eventually, the topological map is fine-tuned with finer distinctions of clusters within areas of the map. After the network has been trained, it can be used as a visualization tool to examine the data structure. Once clusters are identified, neurons in the map can be labeled to indicate their meaning. Assignment of meaning usually requires knowledge on the data and specific application area. 4 Data Mining Applications Neural networks have been used extensively in data mining for a wide variety of problems in business, engineering, industry, medicine, and science.

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