Data Science: Second International Conference, ICDS 2015 by Chengqi Zhang, Wei Huang, Yong Shi, Philip S. Yu, Yangyong
By Chengqi Zhang, Wei Huang, Yong Shi, Philip S. Yu, Yangyong Zhu, Yingjie Tian, Peng Zhang, Jing He
This booklet constitutes the refereed court cases of the second one foreign convention on facts technological know-how, ICDS 2015, held in Sydney, Australia, in August 8-9, 2015.
The 19 revised complete papers and five brief papers offered have been rigorously reviewed and chosen from 31 submissions. The papers specialise in the next issues: mathematical matters in info technological know-how; tremendous information concerns and functions; information caliber and information education; data-driven medical learn; overview and size in information carrier; huge info mining and data administration; case research of knowledge technological know-how; social affects of information science.
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Additional info for Data Science: Second International Conference, ICDS 2015 Sydney, Australia, August 8–9, 2015. Proceedings
2010). Silvescu et al. propose the system EMERSE for classifying and aggregating tweets and text messages about the Haiti earthquake disaster. They train a SVM classiﬁer with the combination of 4 feature sets: unigrams, unigrams with Relief feature selection, abstractions, and topic words generated by LDA (Silvescu et al. 2009). 1 Data Collection Two dataset are collected during the experiment: a microblog dataset and an ILI dataset from the ofﬁcial website. The microblog dataset is mainly used in the experiment and test the ability of new generated model.
5. Sort the n candidate by the value of probability P. 6. Return: Top k of the sorted n candidates as the list of friend recommendation result. The pseudo-code of recommendation algorithm FRUITA is shown in Algorithm 1. In step 3, if calculating each P of the attribute costs time m and there are n attributes, 38 Z. Zhang et al. the time complexity of step 3 is O(mn); in step 4, if calculating each P of the candidates costs time m and there are n candidates, the time complexity of step 4 is O(mn); in step 5, we use the function “Rank()” in SQL to sort the results and the time complexity of step 5 is Oðn log nÞ.
17, 734–749 (2005) 4. : Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 025102 (2001) 5. : Structure of growing social networks. Phys. Rev. E 64, 046132 (2001) A Friend Recommendation System Using Users’ Information 41 6. : Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003) 7. : Emergence of scaling in random networks. Science 286, 509–512 (1999) 8. : Evolution of the social network of scientiﬁc collaborations. Phys. A 311, 590–614 (2002) 9. : A new status index derived from sociometric analysis.