By Petra Perner
This ebook constitutes the refereed lawsuits of the 14th business convention on Advances in information Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The sixteen revised complete papers offered have been rigorously reviewed and chosen from numerous submissions. the themes diversity from theoretical features of information mining to functions of knowledge mining, equivalent to in multimedia info, in advertising, in drugs and agriculture and in method keep watch over, and society.
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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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings
Shibu and others  considered the optimization issues of learning process in this class of systems due to the combined use of traditional procedures for selection of significant features with data Page Rank. Patil  investigated the applicability of Naive Bayes (NB) classifier for learning of web page classification systems within the individual groups of internal features of HTML documents. For classification of web pages Xu et al.  proposed the algorithm called Link Information Categorization (LIC), based on the k nearest neighbors (kNN) method.
Definition 2 (SSOM Node): A SSOM node S in SSOM tree is the combination of page nodes having the identical label; it has eight components, denoted by (tagN ame, content, styleHash, label, parent, children, counter, classif ier), where • tagN ame is the tagName of a page node; • content is the set of content of SOM nodes containing it; • styleHash is the styleHash of a page node; • label is the label of a page node; • parent is the pointer to its parent; • children is the set of pointers to its children; • counter is the number of pages containing it; • classif ier is the 0-1 classiﬁer of S, which can be used to classify a segment into template or inf ormative.
S3 is trained on MAD and a 5k dictionary-annotated corpus with no disambiguation whereas S4 (1K) is trained on MAD and a 1k dictionary-annotated corpus with disambiguation. Finally, UNERD (8) does not train on MAD but uses an window of size 8-words (4 on each side) for disambiguation. A wide range of window sizes was tested, however, the performance results did not vary much. 69 22 Y. Mosallam, A. -G. Ganascia tated text in order to predict entity classes for remaining unannotated text. As illustrated in Fig.