Big Data Analytics and Knowledge Discovery: 17th by Sanjay Madria, Takahiro Hara

By Sanjay Madria, Takahiro Hara

This publication constitutes the refereed court cases of the seventeenth foreign convention on facts Warehousing and data Discovery, DaWaK 2015, held in Valencia, Spain, September 2015.

The 31 revised complete papers provided have been rigorously reviewed and chosen from ninety submissions. The papers are prepared in topical sections similarity degree and clustering; facts mining; social computing; heterogeneos networks and knowledge; facts warehouses; move processing; purposes of massive facts research; and large data.

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

This booklet constitutes the refereed complaints of the seventeenth foreign convention on info Warehousing and data Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The 31 revised complete papers provided have been rigorously reviewed and chosen from ninety submissions. The papers are equipped in topical sections similarity degree and clustering; facts mining; social computing; heterogeneos networks and knowledge; facts warehouses; circulation processing; functions of huge info research; and massive info.

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

Sample text

We compared the accuracy of our technique using the F-measure against the human annotated datasets. The results of the F-measures are shown in Figs. 2 and 3. Table 3 presents the name of each syntactic based experiment, the variation and the shorthand. 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.

Similarly, squares on the upper left quarter of Fig. 2c, which are circled in green and blue, are respectively from the clusters , . Property 3 (Baseline). , the objects are distributed uniformly, all of the object sets for that segment have the same amount of recurrency in the neighborhood DB, which is a function of k, cf. 1. Property 4 (Transitivity). If an object o is not in the neighborhood of p > o, then it is not in the neighborhood of any q > p. Formally let o, p, q ∈ D Ai and o < p < q, o ∈ / k-NN(p) =⇒ o ∈ / k-NN(q).

Abdullatif et al. the similarity between each verb and the rest of the verbs within the set. Thus we will have to make 2k similarity comparisons. For example, if we have a set of verbs {v1 , v2 , v3 }, we will calculate the similarity for (v1 , v2 ), (v1 , v3 ), (v2 , v3 ), (v2 , v1 ), (v3 , v1 ), (v3 , v2 ). Each of the pairs will produce a similarity value, s(vi , vj ) where i and j are the index of two verbs where i = j. We then store the similarity values in a similarity matrix M of dimension k × k.

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