Download e-book for kindle: Big Data Analytics and Knowledge Discovery: 17th by Sanjay Madria, Takahiro Hara

By Sanjay Madria, Takahiro Hara

ISBN-10: 3319227289

ISBN-13: 9783319227283

ISBN-10: 3319227297

ISBN-13: 9783319227290

This ebook constitutes the refereed complaints of the seventeenth foreign convention on info Warehousing and information Discovery, DaWaK 2015, held in Valencia, Spain, September 2015.

The 31 revised complete papers provided have been conscientiously reviewed and chosen from ninety submissions. The papers are geared up in topical sections similarity degree and clustering; facts mining; social computing; heterogeneos networks and knowledge; facts warehouses; movement processing; functions of huge info research; and large data.

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Read Online 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 info for Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings

Example text

We used both syntactic and semantic based approaches. The semantic based results are shown in the last three bars in the figures. In all the experiments we repeated k-means 200 times and averages are reported along with one standard deviation as an error bar with each average. 36 M. Abdullatif et al. F-measure Dong et al. 45 Fig. 2. F-measure on dataset from Dong et al. Cosine LCH PATH WUP F-measure Dong et al. (k = 6) Fig. 3. F-measure on dataset from Dong et al. using k= 6 The number of independent experiments we ran for each test set is 20 where the difference between the experiments is the way the similarity vectors are formed.

Each citation sentence is processed through a semantic role labeler followed by our verb analysis process to extract the main verb relevant to the citations in the sentence. For example, passing the following sentence through our verb analysis process yields the verb “expand”. We expand on the work of James et al. [34] validated by the machine learning community. The relevant verb extraction phase produces a set of relevant verbs V = {v1 , v2 , v3 , . . , vk } where k represents the total number of verbs found.

2. D(LCS(a, b)) is the depth of the lowest common subsumer (deepest common ancestor/parent node of a and b) and L(a, b) is the shortest path between nodes a and b. Using Eq. 2, the Wu-Palmer similarity score between verbs “introduced” and “expands” is as follows: The LCS of both verbs is the root node with a depth of 1. The shortest path is 10. 1667. The Leacock Chodorow similarity measure [13] is shown in Eq. 3 where L(a, b) is the shortest path connecting a and b and Dmax is the maximum depth from the root to the deepest leaf in the hierarchy in which the verbs occur.

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Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings by Sanjay Madria, Takahiro Hara


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