By Sanjay Madria, Takahiro Hara
This ebook constitutes the refereed complaints of the seventeenth overseas convention on facts Warehousing and information Discovery, DaWaK 2015, held in Valencia, Spain, September 2015.
The 31 revised complete papers awarded have been rigorously reviewed and chosen from ninety submissions. The papers are geared up in topical sections similarity degree and clustering; info mining; social computing; heterogeneos networks and knowledge; facts warehouses; movement processing; purposes of massive facts research; and large data.
Read or Download Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings PDF
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Additional resources for Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings
Overall all the top performing measures performs well on the dataset across the experiments. This research is deﬁnitely a good starting point to how unsupervised automatic citation classiﬁcation techniques can be built. 38 M. Abdullatif et al. Teufel et al. 4 Fig. 6. F-measure on dataset from Teufel et al. using k = 12 5 Conclusion and Future Work Citation Classiﬁcation plays an important role in improving the current citation based research evaluation techniques such as the h-index. Most existing citation classiﬁcation techniques perform the classiﬁcation based supervised learning algorithms that require training data and the selection of a citation classiﬁcation scheme.
Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950) 11. : Can retrieval of information from citation indexes be simpliﬁed? multiple mention of a reference as a characteristic of the link between cited and citing article. J. Am. Soc. Inf. Sci. 29(6), 308–310 (1978). 4630290608 12. : Advances in record-linkage methodology as applied to matching the 1985 census of tampa, ﬂorida. J. Am. Stat. Assoc. 84(406), 414–420 (1989) 13. : Combining local context and WordNet similarity for word sense identiﬁcation.
Calculating the similarity between all the pair of relevant verbs from the dataset of citation sentences results in a similarity matrix. Each row in the similarity matrix M represents the similarity between one verb and all the other verbs in the dataset. A row in the matrix is known as a similarity vector, SVvk , for its associated verb, vk . We cluster the vectors representing citations using the well-known clustering algorithm k-means . We chose to use k-means because of its intuitive nature and its ability to allow us to specify the number of clusters we want.