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Shinji Nakadai's Page

Shinji Nakadai

Data Mining Technology Group,
Data Science Research Laboratories,
NEC Corporation

1753, Shimonumabe, Nakahara-ku, Kawasaki-shi, Kanagawa, 211-8666, Japan

Research Field

  • Database, Distributed Computing
  • Machine learning


  • B.S. (2001), School of Engineering, The University of Tokyo
  • M.S. (2003), Graduate School of Engineering, The University of Tokyo
  • Visiting Scholar (2012-13), Department of Computer Science, The University of California, Berkeley, (AMPLab / Prof. Michael J. Franklin).

Conference Presentation

  • Masafumi Oyamada and Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM), 2017 (Regular paper, Acceptance rate: 72/778 = 9.25%)
  • Katsufumi Tomobe, Masafumi Oyamada, and Shinji Nakadai, Link Prediction for Isolated Nodes in Heterogeneous Network by Topic-based Co-Clustering, In Proceedings of the 21st Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), to appear, 2017.
  • M. Asahara, S. Nakadai, and T. Araki, "LoadAtomizer: A locality and I/O load aware task scheduler for MapReduce, " In Proc. of the 4th IEEE Int'l Conf. on Cloud Computing Technology and Science (CloudCom'12), pp. 317-324, 2012.
  • K. Narita, S. Nakadai, and T. Araki, "Landmark-Join: Hash-Join Based String Similarity Joins with Edit Distance Constraints," In Proc. of the 14th Int'l Conf. on Data Warehousing and Knowledge Discovery (DaWaK), pp. 180-191, 2012.
  • H. Tamano, S. Nakadai, and T. Araki, “Optimizing Multiple Machine Learning Jobs on MapReduce,” In Proc. of the 3rd IEEE Int'l Conf. on Cloud Computing Technology and Science(CloudCom'11), pp. 59-66, 2011.
  • S. Nakadai and K. Taniguchi, "Server Capacity Planning with Priority Allocation for Service Level Management in Heterogeneous Server Clusters, " IFIP/IEEE Int'l Symposium on Integrated Network Management(IM2007), 2007.

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