By Cai-Nicolas Ziegler
The textbook handy goals to supply an creation to using computerized tools for collecting strategic aggressive intelligence. Hereby, the textual content doesn't describe a singleton learn self-discipline in its personal correct, corresponding to desktop studying or net mining. It particularly contemplates an application scenario, particularly the collection of information that looks of paramount significance to organisations, e.g., businesses and corporations.
To this finish, the e-book first summarizes the variety of study disciplines that give a contribution to addressing the difficulty, extracting from every one these grains which are of maximum relevance to the depicted software scope. additionally, the booklet provides platforms that placed those options to sensible use (e.g., acceptance tracking structures) and takes an inductive method of outline the gestalt of mining for aggressive strategic intelligence through opting for significant use circumstances which are laid out and defined intimately. those items shape the 1st a part of the book.
Each of these use situations is sponsored by way of a couple of examine papers, a few of that are contained in its mostly unique model within the moment a part of the monograph.
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Extra info for Mining for Strategic Competitive Intelligence: Foundations and Applications
The function decays with distance from the matched neuron, not necessarily in a linear fashion. That is, qi, j (i, j) = 1 while ∀vm,k ∈ N(i, j) : qi, j (m, k) = 0. 10) Clearly, with increasing distance from the matching neuron, the activation effect of the observation on the neurons in the grid becomes weaker and weaker. The above process is repeated until stop conditions based on the number of repetitions or convergence of the grid are met. Self-organizing maps are first and foremost an instrument that is geared for use towards humans: The visualization helps to understand the inherent structure and shape of the data in an intuitive fashion.
Typically, PCA and SVD (see Sec. 1) are put to use, as has been shown in [Viermetz et al, 2008]. , [Witten and Frank, 2005] and [Tomokiyo and Hurst, 2003]): Only the top-n keywords are recorded in each document, so that its corresponding vector describes the presence or absence of these [Kammergruber et al, 2009]. All that has been said for supervised learning and the necessary pre-processing of text also applies to unsupervised learning in like fashion. We will therefore not go into further detail regarding text-based unsupervised learning.
Fig. 10 illustrates a small portion of such a network, while Fig. net; however, note that the scope and content of the site have greatly changed since the time the network structure has been recorded. 30 2 Research Foundations c u d z x k f e m b n o a Fig. 1 SNA in the Context of Competitive Strategic Intelligence So, at which points and in how far can SNA help to acquire and distill competitive strategic intelligence? The first question can be answered by having at look at what the Web of today, the Web of collective intelligence and participation, looks like: It is an interconnected graph where users express their relationships with others; they do so in communities and social media applications such as Facebook, Twitter, LinkedIn, and Xing6 .
Mining for Strategic Competitive Intelligence: Foundations and Applications by Cai-Nicolas Ziegler