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

By Sanjay Madria, Takahiro Hara

This ebook constitutes the refereed court cases of the seventeenth foreign convention on information 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; information mining; social computing; heterogeneos networks and information; info warehouses; movement processing; purposes of huge info research; and large data.

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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

Example text

The information arising from multiple occurrences of an item within a single transaction is disregarded. For example when generating rules such as a customer who buy bread → buy milk we do not consider the quantity of purchase of each of the items, such information may lead to more interesting rules being uncovered. We include this information by including an internal utility component. An item x also receives an external utility value based on the connections between item x and its neighborhood.

S(v2 , vk ) ... SVvk s(vk , v1 ) s(vk , v2 ) . . s(vk , vk ) where M is the number of matches and T is the number of elements in both s and t. The final measure is Hamming distance, whereby we calculate the distance between two sentences by the number of positions at which the corresponding words are different [10]. On top of that we also used a TF-ISF based method that takes into account the word frequencies adjusted by the factor to account for very frequent words and computes the Cosine similarity between the resulting TF-ISF vectors.

4. We note that all three similarity measures are symmetrical, thus the similarity value for s(v1 , v2 ) and s(v2 , v1 ) are the same. In this context the computational complexity of our similarity calculak tions is 22 . Fig. 1. WordNet hierarchy for verbs “expands” and “introduced” Unsupervised Semantic and Syntactic Based Classification 33 The Path similarity measure is calculated as shown in Eq. (1) where L(a, b) is the shortest path connecting verbs a and b in the IS-A (hypernym/hypnoym) taxonomy.

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