The Institute of Scientific and Industrial Research, Osaka University


LAST UPDATE 2017/02/26

  • 研究者氏名
    Researcher Name

    福井健一 Ken-ichi FUKUI
    准教授 Associate Professor
  • 所属
    Professional Affiliation

    The Institute of Scientific and Industrial Research, Osaka University

    Division of Information and Quantum Sciences, Department of Architecture for Intelligence
  • 研究キーワード
    Research Keywords

    data mining
    machine learning
    artificial intelligence
Research Subject
Knowledge Discovery from Event Sequence Data

研究の背景 Background


In many sciences fields, phenomena have become to be electrically recorded, and are ready for analyzing by computers. A scientific methodology using computers based on data is called “data-centric” science, and is expected as the fourth paradigm of the scientific research. Among them, machine learning and data mining techniques, which are inductive approaches, play an important role as a data analysis method. Moreover, estimating interactions among events from an observed event sequence is important for understanding a dynamics of the phenomenon and for their engineering applications.

研究の目標 Outcome


The goal of this research is to establish a knowledge discovery algorithm to discover underlying low or patterns in an observed event sequence data. We have proposed a novel algorithm extracting co-occurrence patterns among events (Fig. 1). Then we have achieved some positive results, as estimation of mechanical interactions among members of a fuel cell from Acoustic Emission event sequence produced by damages (Fig. 2), and also estimation of earthquake interactions from a hypocenter sequence data (Fig. 3).

研究図Research Figure

Fig.1. Conceptual diagram of the proposed co-occurring cluster mining (CCM) algorithm Fig.2. Discovery of co-occurrence damage patterns in a fuel cell, where similar AE events are projected into a two-dimensional map by self-organizing map Fig.3. Discovery of co-occurrence earthquake patterns after the Tohoku Earthquake, showing highly affected area wherein several patterns are shared.

文献 / Publications

K. Fukui, D. Inaba, and M, Numao. “Discovering Seismic Interactions after the 2011 Tohoku Earthquake by Co-occurring Cluster Mining”, Transactions of Japanese Society for Artificial Intelligence, Vol. 29, No. 6, pp. 493-502, 2014.