Big Data Analytics and Knowledge Discovery: 18th by Sanjay Madria, Takahiro Hara

By Sanjay Madria, Takahiro Hara

This e-book constitutes the refereed court cases of the 18th overseas convention on information Warehousing and information Discovery, DaWaK 2016, held in Porto, Portugal, September 2016.

The 25 revised complete papers provided have been rigorously reviewed and chosen from seventy three submissions. The papers are equipped in topical sections on Mining giant information, purposes of huge facts Mining, great information Indexing and looking out, substantial info studying and safeguard, Graph Databases and information Warehousing, info Intelligence and Technology.

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Additional resources for Big Data Analytics and Knowledge Discovery: 18th International Conference, DaWaK 2016, Porto, Portugal, September 6-8, 2016, Proceedings

Example text

1. A Rough Connectedness Algorithm for Mining Communities in Complex Networks 39 Let us consider the time complexity of the proposed algorithm. The key steps of the proposed algorithm are relative connectedness computation, CUA computation, merging operation and fine-tuning operation. Given a network G with n as the total number of nodes and m as the total number of edges, let the average degree of nodes in network G be l = 2 m/n. The complexity of relative connectedness computation is O(n2 log2 l) [23].

In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012) 16. : A two-phase algorithm for fast discovery of high utility itemsets. , Liu, H. ) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005) 17. : Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013) 18. : Efficient algorithms for mining top-K high utility itemsets. IEEE Trans. Knowl. Data Eng. 28(1), 54–67 (2016) 19.

In: ASE BigData & Social Informatics, p. 53 (2015) 10. : Search through systematic set enumeration. Technical Reports (CIS), 297 (1992) 11. : Discovery of high utility itemsets from on-shelf time periods of products. Expert Syst. Appl. 38(5), 5851–5857 (2011) 12. : Efficient algorithms for mining up-to-date high-utility patterns. Adv. Eng. Inf. 29(3), 648–661 (2015) 13. : Mining high-utility itemsets with multiple minimum utility thresholds. In: ACM International Conference on Computer Science & Software Engineering, pp.

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