Reference : Knowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock ...
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/36518
Knowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock Exchanges
English
Lagraa, Sofiane mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Charlier, Jérémy Henri J. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
20-Aug-2018
Yes
No
International
2018 ACM SIGKDD International Conference on Knowledge Discovery and Data mining conference (KDD 2018)
from 19-08-2018 to 23-08-2018
[en] Time series ; crypto-currency ; blockchain ; gradual pattern mining ; graph mining ; knowledge discovery ; imbalanced data
[en] Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one being Bitcoin. However, these crypto-currencies are so far relatively poorly analyzed and investigated. Recently, many solutions, mostly based on ad-hoc engineered solutions, are being developed to discover relevant analysis from crypto-currencies, but are not sufficient to understand behind crypto-currencies. In this paper, we provide a deep analysis of crypto-currencies by proposing a new knowledge discovery approach for each crypto-currency, across crypto-currencies, blockchains, and financial stocks. The novel approach is based on a conjoint use of data mining algorithms on imbalanced time series. It automatically reports co-variation dependency patterns of the time series. The experiments on the public crypto-currencies and financial stocks markets data also demonstrate the usefulness of the approach by discovering
the different relationships across multiple time series sources and insights correlations behind crypto-currencies.
http://hdl.handle.net/10993/36518

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