Download Advances in Data Mining. Applications and Theoretical by Petra Perner PDF

By Petra Perner

This publication constitutes the refereed complaints of the 14th commercial convention on Advances in information Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The sixteen revised complete papers awarded have been rigorously reviewed and chosen from a variety of submissions. the themes diversity from theoretical points of information mining to purposes of knowledge mining, comparable to in multimedia information, in advertising, in drugs and agriculture and in procedure regulate, and society.

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Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings PDF

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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings

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The classification is performed by nine categories. In general, it should be noted that most often used features that are applied for web page classification are extracted from the page text content. For instance, Dumais and Chen [6] separated concepts of web page text, header information and descriptive information service tag “meta”. They implemented the Support Vector Machine (SVM) method. Lai and Wu [18] used two approaches to obtain necessary features for classification: meaningful term extraction and discriminative term selection.

Lingvisticae Investigationes 30(1), 3–26 (2007) 3. : Fine grained classification of named entities. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics (2002) Unsupervised Named Entity Recognition and Disambiguation 23 4. : Proper name extraction from non-journalistic texts. Language and Computers 37(1), 144–157 (2001) 5. : Language independent named entity recognition combining morphological and contextual evidence.

We experiment with three different configurations for classification tasks using all possible pairs of product categories. The configurations are summarized in Table 3. For each pair, we train the NB classifier using a Training Set and then run the classifier over the corresponding Test Set. In Table 4, we can observe that classification results based on all the cleaning methods are dramatically better than the results using the original noisy Web pages. Our method produce a little better result than SST and much better than the other three baselines for Web page classification.

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