臺灣博碩士論文加值系統

As cryptocurrency drawing its attention to the public and its popularity rises, investments in cryptocurrency also rise at a rapid speed. For having the rules to follow in forecasting the appreciation or depreciation of the cryptocurrency prices and to minimize investment risk are of great interest to investors.
Association rule mining by the Apriori algorithm is applied as the data mining technique in this research to find such rules. This study examines the relationship between ten commonly known cryptocurrencies and eleven common foreign currencies’ exchange rates to New Taiwan Dollar (NTD). Data collection periods for the prices of cryptocurrencies and the different foreign currencies are from July 27, 2016 to January 3, 2019, with a total of 605 transaction dates that are used as the basis of analysis. This study finds interesting association rules on the appreciation and depreciation of the cryptocurrency prices and the foreign currency exchange rates based on NTD, with varying thresholds of minimum support and minimum confidence. The rules generated are all with lift values of greater than 1, meaning that the rules are interesting and meaningful. In addition, of the ten cryptocurrencies studied, Bitcoin appears to be most sensitive to fiat currency exchange rates as compared to other nine cryptocurrencies. Investors on cryptocurrency may find the results from our research to be interesting knowledge and may apply the rules found as referrals when making investment decisions.

TABLE OF CONTENTS

DEDICATION I
ACKNOWLEDGEMENTS II
ABSTRACT III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII

CHAPTER I. Introduction 1
1.1 Background 1
1.2 Purpose 4
1.3 Research Method 5
1.4 Research Framework 8

CHAPTER II. Literature Review 10
2.1 Data mining and knowledge discovery 10
2.2 Data Mining Methodology 13
2.3 Application of Association Rule 16
2.4 Literature review in prediction of cryptocurrency prices 19

CHAPTER III. Research Methodology 22
3.1 Principles of Association Rules 22
3.2 The Apriori Algorithm 26

CHAPTER IV. Data Analysis and Results 29
4.1 Software and Hardware Environment 29
4.2 Data Collection 29
4.3 Data Pre-processing 32
4.4 Data Mining Execution 34
4.5 Results and Interpretations 41

CHAPTER V. Conclusion and Future Research 46
5.1 Conclusions 46
5.2 Study Limitations 47
5.3 Future Research 47

REFERENCES 49
Publications 49
Internet References 51

REFERENCES
Publications
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Paper presented at the Acm sigmod record.
Argiddi, R. V., & Apte, S. (2012). Future trend prediction of indian it stock market using association rule mining of transaction data. International Journal of Computer Applications, 39(10), 30-34.
Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and data Engineering, 8(6), 866-883.
Ciaian, P., Rajcaniova, M., & Kancs, d. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
Djatna, T., & Alitu, I. M. (2015). An application of association rule mining in total productive maintenance strategy: an analysis and modelling in wooden door manufacturing industry. Procedia Manufacturing, 4, 336-343.
Faghmous, J. H., & Kumar, V. (2014). Spatio-temporal data mining for climate data: Advances, challenges, and opportunities. In Data mining and knowledge discovery for big data (pp. 83-116): Springer.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996a). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996b). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. Paper presented at the KDD.
Ghousi, R., Mehrani, S., Momeni, M., & Anjomshoaa, S. (2012). Application of data mining techniques in drug consumption forecasting to help pharmaceutical industry production planning. Paper presented at the Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management.
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12): Springer Science & Business Media.
Grupe, F. H., & Mehdi Owrang, M. (1995). Data base mining discovering new knowledge and competitive advantage. Information System Management, 12(4), 26-31.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques: Elsevier.
Hileman, G., & Rauchs, M. (2017). Global cryptocurrency benchmarking study. Cambridge Centre for Alternative Finance, 33.
Huang, C. K., & Li, C. J. (2018). Substitution association rule mining in the stock market with big data analysis. Paper presented at the The 24th Cross Strait Conference of Information Management Development and Strategy, The University of Hong Kong, Hong Kong.
Kamsu-Foguem, B., Rigal, F., & Mauget, F. (2013). Mining association rules for the quality improvement of the production process. Expert systems with applications, 40(4), 1034-1045.
Kim, Y. B., Kim, J. G., Kim, W., Im, J. H., Kim, T. H., Kang, S. J., & Kim, C. H. (2016). Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PloS one, 11(8), e0161197.
Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific reports, 3, 3415.
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining: John Wiley & Sons.
Li, X., & Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49-60.
Na, S. H., & Sohn, S. Y. (2011). Forecasting changes in Korea composite stock price index (KOSPI) using association rules. Expert systems with applications, 38(7), 9046-9049.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
Peng, Y., Albuquerque, P. H. M., de Sá, J. M. C., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert systems with applications, 97, 177-192.
Phillips, R. C., & Gorse, D. (2017). Predicting cryptocurrency price bubbles using social media data and epidemic modelling. Paper presented at the 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Sapovadia, V. (2015). Legal Issues in Cryptocurrency. In Handbook of Digital Currency (pp. 253-266): Elsevier.
Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of Economics and Financial Analysis, 2(2), 1-27.
Steinert, L., & Herff, C. (2018). Predicting altcoin returns using social media. PloS one, 13(12), e0208119.
Sulova, S. (2018). Association Rule Mining for Improvement of IT Project Management. artificial intelligence, 6, 7.
Szalkai, B., Grolmusz, V. K., & Grolmusz, V. I. (2017). Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimers database. Archives of gerontology and geriatrics, 73, 300-307.
Vazan, P., Janikova, D., Tanuska, P., Kebisek, M., & Cervenanska, Z. (2017). Using data mining methods for manufacturing process control. IFAC-PapersOnLine, 50(1), 6178-6183.
Zhao, Y. (2015). Cryptocurrency Brings New Battles into the Currency Market. Future Internet (FI) and Innovative Internet Technologies and Mobile Communications (IITM), 91.

Internet References
CCN (Sep 10, 2018), Jack Ma: I Pay Special Attention to Blockchain & Bitcoin to Create Cashless Society. Source: https://www.ccn.com/jack-ma-i-pay-special-attention-to-blockchain-bitcoin-to-create-cashless-society
CNBC (Feb 25, 2019), Warren Buffett says bitcoin is a ‘delusion’ and ‘attracts charlatans’. Source: https://www.cnbc.com/2019/02/25/warren-buffett-says-bitcoin-is-a-delusion.html
Cointelegraph 1 (May 1, 2019), Crypto Markets Recover With Bitcoin Breaking $5,300, Gold and Oil Prices Rise. Source: https://cointelegraph.com/news/crypto-markets-recover-with-bitcoin-breaking-5-300-gold-and-oil-prices-rise
Cointelegraph 2 (Apr 30, 2019), Fundstrat’s Tom Lee Predicts New All-Time Highs for Crypto by 2020. Source: https://cointelegraph.com/news/fundstrats-tom-lee-predicts-new-all-time-highs-for-crypto-by-2020
IG (September 5, 2018), The top 10 most traded currencies in the world. Source: https://www.ig.com/au/trading-strategies/the-top-ten-most-traded-currencies-in-the-world-180904
Library of Congress (June 2018), Regulation of Cryptocurrency Around the World. Source: https://www.loc.gov/law/help/cryptocurrency/world-survey.php
Investopedia (April 27, 2019), The 10 Most Important Cryptocurrencies Other Than Bitcoin. Source: https://www.investopedia.com/tech/most-important-cryptocurrencies-other-than-bitcoin/
Investopedia (May 5, 2019a), The Top 8 Most Tradable Currencies. Source: https://www.investopedia.com/trading/most-tradable-currencies/
Reuters (Apr 9, 2019), China wants to ban bitcoin mining. Source: https://www.reuters.com/article/us-china-cryptocurrency/china-wants-to-ban-bitcoin-mining-idUSKCN1RL0C4
Telegraph (May 25, 2018), The 10 most popular cryptocurrencies in 2018. Source: https://www.telegraph.co.uk/technology/digital-money/top-10-popular-cryptocurrencies-2018/
Wikipedia (2019), Template: Most traded currencies. Source: https://en.wikipedia.org/wiki/Template:Most_traded_currencies