By Sanjay Madria, Takahiro Hara
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 awarded have been conscientiously reviewed and chosen from ninety submissions. The papers are geared up in topical sections similarity degree and clustering; info mining; social computing; heterogeneos networks and knowledge; info warehouses; flow processing; functions of huge info research; and large data.
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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
Focusing on high utility items and itemsets has two major beneﬁts: ﬁrstly, a smaller set of potentially useful patterns and rules are presented to the user; secondly, the execution time overhead for pattern and rule generation are substantially reduced. Our experimental results clearly illustrate the advantages of the inﬂuence factor measure in achieving these two beneﬁts. A recent approach using a utility mining [5,7,11] model was proposed to discover knowledge from a dataset. Using utility mining, several important business objectives such as maximizing revenue or minimizing inventory costs  can be considered, and in turn knowledge about itemsets contributing to the majority of the proﬁt can be discovered.
In the recent years, the number of scientiﬁc publications has increased substantially. A way to measure the impact of a publication is to count the number of citations to the paper. Thus, citations are being used as a proxy for a researcher’s contribution and inﬂuence in a ﬁeld. Citation classiﬁcation can provide context to the citations. To perform citation classiﬁcation, supervised techniques are normally used. To the best of our knowledge there are no research that performs this task in a unsupervised manner.
PVLDB 2(1), 1270–1281 (2009) 15. : Gene expression-based classiﬁcation of malignant gliomas correlates better with survival than histological classiﬁcation. Cancer Res. 63(7), 1602–1607 (2003) 16. : Reversed CF: a fast collaborative ﬁltering algorithm using a k-nearest neighbor graph. Expert Syst. Appl. 42(8), 4022–4028 (2015) 17. : Clustering by fast search and ﬁnd of density peaks. Sci. 344(6191), 1492–1496 (2014) 18. : Fast parameterless density-based clustering via random projections. In: CIKM, CIKM 2013, pp.
Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings by Sanjay Madria, Takahiro Hara