Education

• Mar. 2006, Master of Economics
  Graduate School of Economics, Osaka Prefecture University
  (Supervisor: Prof. Hiroyuki Morita)
• Mar. 2009, Doctor of Economics
  Graduate School of Economics, Osaka Prefecture University
  (Supervisor: Prof. Hiroyuki Morita)

Professional Experiences

• April 2007 – March 2009
  Research Fellow of Japan Society for the Promotion of Science (DC2)
• April 2009 – March 2012
  Assistant professor, Faculty of commerce, Kansai University
• April 2012 – December 2013
  Director of Magne-Max Capital Management
• April 2012 – March 2014
  Adjunct Researchers, Data Mining Research Center, Kansai University
• April 2014 – March 2016
  Lecture, School of commerce, Senshu University
• April 2016- (Present)
  Associate Professor, School of commerce, Senshu University

Research Areas

• Application of Data Mining
• Marketing Science and Consumer Behavior
• Data Analysis (Machine Learning, Statistics)
• Operations Research

Book

• H. Morita and T. Nakahara: Pattern mining for historical data analysis by using moea (Chapter 3). Multiobjective Programming and Goal Programming, Theoretical Results and Practical Applications (Lecture Notes in Economics and Mathematical Systems). Barichard, V. and M. Ehrgott. and X. Gandibleux and V. T’Kindt. Eds., Springer-Verlag, pp.135-144, 2009. March.
• T. Nakahara and H. Morita: Finding hierarchical patterns in large pos data using historical trees (Chapter 4). Data Mining for Design and Marketing. Ohsawa, Y and K. Yada, Eds., Chapman and Hall, pp.57-79, 2009. January.

Journal

1. T. Nakahara, ``Why Do We Love Coffee Even Though It Is Bitter?``, In: Meiselwitz G. (eds) Social Computing and Social Media: Experience Design and Social Network Analysis. HCII 2021. Lecture Notes in Computer Science, vol 12774. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_30.
2. T. Sato, Y. Takano, T. Nakahara, “Investigating consumers’ store-choice behavior via hierarchical variable selection,” Advances in Data Analysis and Classification, 2018.
3. T. Uno, H. Maegawa, T. Nakahara, Y. Hamuro, R. Yoshinaka, M. Tatsuta, “Micro-clustering by data polishing”, 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, pp. 1012-1018, Dec., 2017.
4. S. Cheung, Y. Hamuro, J. Mahlich, T. Nakahara, R. Sruamsiri, S. Tsukazawa, ”Drug Utilization of Japanese Patients Diagnosed with Schizophrenia: An Administrative Database Analysis”, Clinical Drug Investigation, Vol. 37, Issue 6, pp.559-569, 2017/6.
5. T. Sato, Y. Takano, T. Nakahara, “Using Mixed Integer Optimisation to Select Variables for a Store Choice Model,” International Journal of Knowledge Engineering and Soft Data Paradigms, Vol.5, No.2, pp.123–134 (2016).
6. T. Nakahara, K. Yada, “Analyzing consumers’ shopping behavior using RFID data and pattern mining”, Advances in Data Analysis and Classification, Springer, Vol. 6, Issue 4 (2012), Page 355-365, December.
7. T. Nakahara, K. Yada, “Evaluation of the Shopping Path to Distinguish Customers Using a RFID Dataset”, International Journal of Organizational and Collective Intelligence, 2(4), 1-14, October-December 2011.

International Conference

1. T. Nakaara, Y. Sakuma, K. Yada, M. Wedel, ''The Impact of Checkout Congestion on Purchasing Behavior'', EMAC 2021 Annual conference (Online), May 25th, 2021.
2. T. Nakahara, A. Ouchi, Y. Hamuro, ''Using Social Networking Services in Education to Support Learning'', The International Symposium on Business and Social Sciences, Hawaii, USA, 6-8 August 2019. pp.226-234.
3. T. Nakahara, “Use of Personal Color and Purchasing Patterns for Distinguishing Fashion Sensitivity”, In: Meiselwitz G. (eds) Social Computing and Social Media. Technologies and Analytics. SCSM 2018. Lecture Notes in Computer Science, vol 10914. Springer, Cham, https://doi.org/10.1007/978-3-319-91485-5_20. ISBN: 978-3-319-91484-8, 2018.
4. T. Nakahara, T. Uno, and Y. Hamuro, “Prediction Model Using Micro-clustering”, Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014 Gdynia, Poland, September 2014 Proceedings, DOI: 10.1016/j.procs.2014.08.231, Volume 35, 2014, pp.1488–1494, 2014.
5. T. Nakahara, Y. Hamuro, “Detecting topics from Twitter posts during TV program viewing”, MoDAT in conjunction with IEEE ICDM 2013 in Dallas, Texas. DOI: 10.1109/ICDMW.2013.48. December 7, 2013.
6. T. Nakahara, K. Yada, “Extraction of Customer Potential Value Using Unpurchased Items and In-store Movements”, In proceedings of KES 2011, KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, Lecture Notes in Computer Science, 2011, Volume 6883/2011, DOI: 10.1007/978-3-642-23854-3_31. pp.295-303, September 12-14, 2011. Kaiserslautern, Germany.
7. M. Kholod, T. Nakahara, H. Azuma, K. Yada, “The Influence of Shopping Path Length on Purchase Behavior in Grocery Store“, In proceedings of KES 2010, KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, Lecture Notes in Computer Science, 2010, Volume 6278/2010, DOI: 10.1007/978-3-642-15393-8_31. pp.273-280, September.
8. T. Nakahara, T. Uno, K. Yada, ”Extracting Promising Sequential Patterns from RFID Data Using the LCM Sequence”, In proceedings of KES 2010, KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, Lecture Notes in Computer Science, 2010, Volume 6278/2010, DOI: 10.1007/978-3-642-15393-8_28. pp.244-253, September.
9. H. Morita, T. Nakahara, Y. Hamuro, S. Yamamoto, ”Decision Tree-based Classifier Incorporating Contrast Patterns”, The 13th IEEE International Symposium on Consumer Electronics(ISCE2009), DOI: 10.1109/ISCE.2009.5156927, pp.858-860. May 25-28, 2009, Mielparque-Kyoto, Kyoto, Japan, 2009.
10. T. Nakahara, H. Morita, “Recommender System for Music CDs Using a Graph Partitioning Method”, In proceedings of KES 2009, Lecture Notes in Computer Science 5712, Springer 2009. pp.259-269, September.
11. T. Nakahara, H. Morita, “Pattern Mining in POS data using a Historical Tree”, Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong, China, IEEE Computer Society, 2006, pp.570-574.