Data Mining

Data mining or Knowledge Discovery in Databases, has been recognized as one of the most emerging fields of computer science and information technology for the past two decades. It is an interdisciplinary field, combining database technology, artificial intelligence, machine learning, statistics and algorithms on an ever growing spectrum of applications, such as financial modeling, molecular biology, document classification, web mining etc.

Mining for weak periodicities in large, real world time series is a research subfield that uses Digital Signal Processing tools along with efficient algorithms in order to produce accurate results from data that often contain noise and randomness.

Contact Person(s)

For further information please contact:

Berberidis Christos

Academic Associate, School of Science and Technology

Tel: +30 2310 807534
c.berberidis@ihu.edu.gr

Tjortjis Christos

Assistant Professor, School of Science and Technology

Selected Publications

  • Kanellopoulos Y., Antonellis P., Tjortjis C., Makris C. and Tsirakis N., "k-Attractors: A Partitional Clustering Algorithm for Numeric Data analysis", Applied Artificial Intelligence, Taylor & Francis, Vol. 25, No.2, pp. 97-115 (2011) • Journal Paper
  • Antonellis P., Antoniou D., Kanellopoulos Y., Makris C., Theodoridis E., Tjortjis C. and Tsirakis N., "Clustering for Monitoring Software Systems Maintainability Evolution", Electronic Notes in Theoretical Computer Science, Elsevier, Vol. 233, pp. 43-57 (2009) • Journal Paper
  • Zhang S., Tjortjis C., Zeng X., Qiao H., Buchan I. and Keane J., "Comparing Data Mining Methods with Logistic Regression in Childhood Obesity Prediction", Information Systems Frontiers Journal, Springer, Vol. 11, No. 4, pp. 449-460 (2009) • Journal Paper
  • Antonellis P., Antoniou D., Kanellopoulos Y., Makris C., Theodoridis E., Tjortjis C. and Tsirakis N., "Employing Clustering for Assisting Source Code Maintainability Evaluation according to ISO/IEC-9126", Proc. Artificial Intelligence Techniques in Software Engineering Workshop (AISEW 2008) (2008) • Conference Paper
  • Kanellopoulos Y., Heitlager I., Tjortjis C., and Visser J., "Interpretation of Source Code Clusters in Terms of the ISO/IEC-9126 Maintainability Characteristics", Proc. 12th European Conf. Software Maintenance and Reengineering (CSMR 2008), pp. 63-72 (2008) • Conference Paper

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