By Petra Perner
This publication constitutes the refereed complaints of the 14th commercial convention on Advances in info Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The sixteen revised complete papers offered have been conscientiously reviewed and chosen from numerous submissions. the subjects diversity from theoretical features of information mining to purposes of information mining, equivalent to in multimedia info, in advertising, in drugs and agriculture and in technique regulate, and society.
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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
In this section, we describe the site-oriented segment object model. 1 Representation of Web Pages Unlike plain text document, Web pages consist of text contents and tags. Each page can be represented as a DOM (Document Object Model) tree, in which tags are internal nodes and the detailed texts, images or hyperlinks are leaf nodes. In this paper we expand the original DOM tree by adding some additional attributes to the nodes. We call the modiﬁed structure as SOM (Segment Object Model) tree, the node of which is deﬁned as follows: Definition 1 (SOM Node): A SOM node N has six components, denoted by (tagN ame, content, styleHash, label, parent, children), where 28 B.
In: COLING, vol. 96, pp. 466–471 (1996) 2. : A survey of named entity recognition and classification. 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.
Gao and Q. Fan Algorithm 5. label then 3: if S is a leaf node then 4: Add the descendant nodes into N . 5: end if 6: Consider N as a segment seg. classif ier. label then 13: T ← T ∪ DetectTemplate(s, n); 14: end if 15: end for 16: end if 17: Return T ; 18: end procedure can detect all the template segments by mapping and classifying every segments recursively. 5 Experiments This section evaluates our proposed informative contents discovering algorithm. , clustering and classiﬁcation, to test our system.
Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings by Petra Perner