By Cai-Nicolas Ziegler

The textbook handy goals to supply an creation to using automatic equipment for accumulating strategic aggressive intelligence. Hereby, the textual content doesn't describe a singleton examine self-discipline in its personal correct, equivalent to desktop studying or net mining. It quite contemplates an application scenario, specifically the collection of information that looks of paramount significance to companies, e.g., businesses and corporations.

To this finish, the ebook first summarizes the diversity of study disciplines that give a contribution to addressing the problem, extracting from every one these grains which are of extreme relevance to the depicted software scope. furthermore, the ebook provides structures that placed those recommendations to useful use (e.g., acceptance tracking systems) and takes an inductive method of outline the gestalt of mining for aggressive strategic intelligence via determining significant use situations which are laid out and defined intimately. those items shape the 1st a part of the book.

Each of these use situations is subsidized by way of a few study papers, a few of that are contained in its mostly unique model within the moment a part of the monograph.

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Additional resources for Mining for Strategic Competitive Intelligence: Foundations and Applications

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The next paragraphs will briefly outline several clustering algorithms in use today. Some of these have been employed in our own publications comprised in this book, namely density-based clustering in [Kammergruber et al, 2009], and expectation maximization in [Viermetz et al, 2008]. Moreover, in [Ziegler et al, 2008], we have introduced a new clustering approach for clustering small textual documents. 1 k-Means Clustering Being one of the earliest clustering algorithms, k-means [MacQueen, 1967] is still in common use today.

2 Expectation Maximization Expectation maximization (EM) is similar to k-means in that it requires the number k of clusters to be fixed beforehand. 3 Data Mining 23 unobserved latent variables. EM is an iterative method which alternates between performing an expectation (“E”) step and a maximization (“M”) step. The “E” step computes an expectation of the log-likelihood with respect to the current estimate of the distribution for the latent variables. On the other hand, the “M” step computes the parameters which maximize the expected log-likelihood found on the expectation step.

The following paragraphs will give an excerpt of techniques in both NLP and text mining, focusing primarily on those methods that are relevant for the proper understanding of the application context at hand. Note that this brief survey can neither provide an exhaustive view on both fields nor cover all the techniques and methods used in our research work. For instance, sentence splitting techniques and POS tagging (see [Ziegler and Skubacz, 2007] and [Ziegler et al, 2008]) are not laid out in this survey.

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