Download e-book for kindle: Data Mining With Ontologies: Implementations, Findings and by Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel

By Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel Hugo Xodo

ISBN-10: 1599046180

ISBN-13: 9781599046181

ISBN-10: 1599046202

ISBN-13: 9781599046204

Essentially the most very important and not easy difficulties in info mining is the definition of previous wisdom both from the method or the area. previous wisdom is useful for choosing appropriate information and mining options, pruning the distance of speculation, representing the output in a understandable approach, and enhancing the general strategy. facts Mining with Ontologies: Implementations, Findings, and Frameworks offers a accomplished set of methodologies and instruments for the advance of ontological foundations for information mining in assorted domain names starting from biomedicine to advertising and marketing. Forming a benchmark reference for destiny efforts to augment features in ontology usage and layout, this most excellent Reference resource could be a useful addition to libraries all over the world.

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Extra info for Data Mining With Ontologies: Implementations, Findings and Frameworks (Premier Reference)

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A second important use of this data is for finding interesting marketing knowledge. The data may be mined for useful correlations between interests and also between interests and demographic categories. If someone is interested in The Simpsons, what is the likelihood that he is interested in another comedy? What age groups are interested in particular types of TV series? The available data can be used for such investigations. The results may again be useful for marketing. In the Web Marketing Project, we collected people’s demographic and interest information from home pages and stored them in a database.

If a rule is not qualified, it does not meet the expectations of a useful rule. Therefore, to discard such a rule of little use and to lose those details is reasonable. On the other hand, the generalization has the advantage that it provides better indicators for new product promotions. A new product would never appear in any existing rule, thus no exact match can be found. However, it is not a hard problem to categorize the new product into an existing category, or a higher level interest. For example, the 2005 TV comedy “American Dad” had not been listed in Yahoo at the time of this research, that is, no rule can be found for it by mining.

However, the new definition only returns one tuple for each person. , Aik_ mi>, all at level k. If Ni is at a higher level with a number less than k, it does not have an ancestor at level k. In this case, Aik_1= Ni. Therefore, Rk is defined as the operation that takes T as input and returns the raised tuple (please see equation (1)) as output for every T, except for the tuple . For the latter Rk (T) is undefined. Moreover, a duplication check is performed during Raising. Thus, every Aik_ j that appears in Tk is unique and all the duplicates have been removed.

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Data Mining With Ontologies: Implementations, Findings and Frameworks (Premier Reference) by Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel Hugo Xodo


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