{"id":4096,"date":"2021-05-31T20:30:35","date_gmt":"2021-05-31T17:30:35","guid":{"rendered":"https:\/\/www.ihu.gr\/ucips\/?page_id=4096"},"modified":"2022-09-12T10:10:54","modified_gmt":"2022-09-12T07:10:54","slug":"christos-tjortjis","status":"publish","type":"page","link":"https:\/\/www.ihu.gr\/ucips\/cv\/christos-tjortjis","title":{"rendered":"Christos Tjortjis"},"content":{"rendered":"

Christos Tjortjis<\/h2>\n

\"\"<\/p>\n

Personal web page<\/a><\/p>\n

Dr. Christos Tjortjis is the Dean of the School of Science and Technology, International Hellenic University<\/a> and Assoc. Prof. in Knowledge Discovery and Software Engineering systems. He is Programme Director for the MSc in <\/a>Data Science<\/a>, the MSc in ICT Systems,<\/a> the MSc in Mobile and Web Computing: Internet of Things Applications<\/a>, the MSc in Cybersecurity<\/a>, and the MSc in Smart Cities and Communities<\/a> at the International Hellenic University<\/a>, School of Science & Technology<\/a>. Formerly he was an adjunct Assoc. Prof. at the University of Ioannina, Dept. of Computer Science & Engineering, an adjunct Assist. Prof. at the University of W. Macedonia, Dept. Engineering Informatics and Telecommunications, a tenured Lecturer at the University of Manchester, Schools of Computer Science and Informatics, as well as UMIST, Department of Computation.<\/p>\n

He holds a Deng(Hons) in Computer Engineering and Informatics (5 year studies) from the Department of Computer Engineering & Informatics at the University of Patras, and a BSc(Hons) in Law (4 year studies) from the Department of Law at the Democritus University of Thrace, in Greece. He also holds an MPhil in Computation from UMIST and a PhD in Informatics from the University of Manchester, U.K.<\/p>\n

His focal research area is data mining, decision support, software engineering and smart cities, and his aim is to advance the use of data mining in domains such as medicine, programming languages and novel types of heterogeneous data. His research interests are in the areas of data mining and analytics, and software management, where he has published widely. His research objectives include bridging the gap between theories and applications of data mining, as well as establishing novel ways of retrieving information from data, text, social media and source code. He published over 90 papers in int\u2019l journals and conferences. He was on the Program Committee of more than 100 and on the Organizational Committee of 9 conferences \/ workshops. He has been involved in several projects concerning data and text mining, data recovery, software quality and evolution etc., leading 7 of them.<\/p>\n

Selected\u00a0Publications<\/strong><\/p>\n

    \n
  1. P. Koukaras, C. Tjortjis<\/strong> and D. Rousidis, \u2018Mining Association Rules from COVID-19 Related Twitter Data to Discover Word Patterns, Topics and Inferences\u2019, Information Systems, Vol. 109, 2022 (Elsevier) \u2022 Journal Paper<\/li>\n
  2. P. Koukaras, C. Tjortjis<\/strong>, P. Gkaidatzis, N. Bezas, D. Ioannidis, and D. Tzovaras, \u2018An interdisciplinary approach on efficient virtual microgrid to virtual microgrid energy balancing incorporating data preprocessing techniques\u2019, Computing, Vol. 104, No. 1, pp. 209-250, 2022 (Springer) \u2022 Journal Paper.<\/li>\n
  3. P. Koukaras, C. Nousi and C. Tjortjis<\/strong>, \u2018Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning\u2019, Telecom, 3(2), 358-378, 2022 (MDPI) \u2022 Journal Paper<\/li>\n
  4. A. Mystakidis, E. Ntozi, K. Afentoulis, P. Koukaras, G. Giannopoulos, N. Bezas, P. Gkaidatzis, D. Ioannidis, C. Tjortjis<\/strong> and D. Tzovaras, \u201cOne Step Ahead Energy Load Forecasting: Multi-model approach utilizing Machine and Deep Learning\u201d, Proc. 57th Int\u2019l Universities Power Engineering Conf., 2022 (IEEE) \u2022 Conference Paper<\/li>\n
  5. A. Ahmed, C. Tjortjis<\/strong>, \u201cMachine Learning based IoT-BotNet Attack Detection Using Real-time Heterogeneous Data\u201d, Proc. 2nd Int\u2019l Conf. on Electrical, Computer and Energy Technologies, 2022 \u2022 Conference Paper<\/li>\n
  6. \u0395. Kapoteli, P. Koukaras, C. Tjortjis<\/strong>, Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case studies in English and Greek language, Proc. 18th Int\u2019l Conf. Artificial Intelligence Applications and Innovations. (Springer) \u2022 Conference Paper<\/li>\n
  7. M. Karagkiozidou, P. Koukaras, C. Tjortjis<\/strong>, Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline, Proc. 18th Int\u2019l Conf. Artificial Intelligence Applications and Innovations. (Springer) \u2022 Conference Paper<\/li>\n
  8. P. Koukaras, A. Dimara, S. Herrera, N. Zangrando, S. Krinidis, D. Ioannidis, P. Fraternali, C. Tjortjis<\/strong>, C.-N. Anagnostopoulos, D. Tzovaras, Proactive buildings: A prescriptive maintenance approach, Proc. 18th Int\u2019l Conf. Artificial Intelligence Applications and Innovations. (Springer) \u2022 Conference Paper<\/li>\n
  9. N. Zangrando, S. Herrera, P. Koukaras, A. Dimara, P. Fraternali, S. Krinidis, D. Ioannidis, C. Tjortjis<\/strong>, C.-N. Anagnostopoulos, D. Tzovaras, Anomaly detection in small-scale industrial and household appliances, Proc. 18th Int\u2019l Conf. Artificial Intelligence Applications and Innovations. (Springer) \u2022 Conference Paper<\/li>\n
  10. M. Vlachos Giovanopoulos, G. Michailidis, P. Koukaras and C. Tjortjis<\/strong>, \u201cHealthcare support using Data Mining: A case study on stroke prediction\u201d, Handbook of Artificial Intelligence in Healthcare, 2022 (Springer) \u2022 Book chapter<\/li>\n
  11. V. Chouliara, E. Kapoteli, P. Koukaras and C. Tjortjis<\/strong>, \u201cSocial Media Sentiment Analysis related to COVID-19 Vaccinations\u201d, Handbook of Artificial Intelligence in Healthcare, 2022 (Springer) \u2022 Book chapter<\/li>\n
  12. T. Chatzinikolaou, E. Vogiatzi, A. Kousis, C. Tjortjis, \u201cSmart Healthcare Support Using Data Mining and Machine Learning\u201d, IoT and WSN based Smart Cities: A Machine Learning Perspective, pp 27\u201348. 2022 (Springer) \u2022 Book chapter<\/li>\n
  13. C. Nousi, P. Belogianni, P. Koukaras and C. Tjortjis<\/strong>, \u201cMining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications\u201d, Handbook of Artificial Intelligence in Healthcare, Intelligent Systems Reference Library, vol 211, pp. 287-312, 2022 (Springer) \u2022 Book chapter<\/li>\n
  14. P. Koukaras, N. Bezas, P. Gkaidatzis, D. Ioannidis, D. Tzovaras, and C. Tjortjis<\/strong>, \u2018Introducing a Novel Approach in One-step Ahead Energy Load Forecasting\u2019, Sustainable Computing: Informatics and Systems, Vol. 32, 100616, 2021 (Elsevier) \u2022 Journal Paper<\/li>\n
  15. S. Liapis, K. Christantonis, V. Chazan-Pantzalis, A. Manos, D.E. Filippidou, and C. Tjortjis<\/strong>, \u2018A methodology using classification for traffic prediction: Featuring the impact of COVID-19\u2019, Integrated Computer-Aided Engineering, Vol. 28, pp. 417-435, 2021 (IOS Press) \u2022 Journal Paper<\/li>\n
  16. V. Sarlis, V. Chatziilias, C. Tjortjis<\/strong>, D. Mandalidis, \u2018A Data Science Approach Analysing the Impact of Injuries on Basketball Players and Team Performance\u2019, Information Systems, Vol. 99, 2021 (Elsevier) \u2022 Journal Paper.<\/li>\n
  17. P. Koukaras, P. Gkaidatzis, N. Bezas, T. Bragatto, M. Antal, F. Carere, D. Ioannidis, C. Tjortjis<\/strong> and D. Tzovaras, \u2018A Tri-layer Optimization Framework for One-day Ahead Energy Scheduling based on Cost and Discomfort Minimization\u2019, Energies, Vol. 14, no 12, 3599, 2021 (MDPI) \u2022 Journal Paper<\/li>\n
  18. A. Kousis and C. Tjortjis<\/strong>, \u2018Data Mining Algorithms for Smart Cities: A Bibliometric Analysis\u2019, Algorithms, Vol. 14, no. 8, 242, 2021 (MDPI) \u2022 Journal Paper<\/li>\n
  19. P. Koukaras, D. Rousidis and C. Tjortjis<\/strong>, \u2018Introducing a novel Bi-functional method for Exploiting Sentiment in Complex Information Networks\u2019, Int\u2019l Journal of Metadata, Semantics and Ontologies. Vol. 15, No. 3, 2021 (Inderscience) \u2022 Journal Paper<\/li>\n
  20. A. Mystakidis, N. Stasinos, A. Kousis, V. Sarlis, P. Koukaras, D. Rousidis, I. Kotsiopoulos, C. Tjortjis<\/strong>, \u2018Predicting Covid-19 ICU needs using Deep Learning, XGBoost and Random Forest Regression with the Sliding Window technique\u2019, IEEE Smart Cities, 2021 (IEEE) \u2022 Journal Paper<\/li>\n
  21. P. Koukaras, V. Tsichli, and C. Tjortjis<\/strong>, \u201cPredicting Stock Market Movements with Social Media and Machine Learning\u201d, Proc. 17th Int\u2019l Conf. on Web Information Systems and Technologies, 2021. \u2022 Conference Paper<\/li>\n
  22. C. Nousi and C. Tjortjis<\/strong>, \u201cA Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and StockTwits Data\u201d, Proc. 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, 2021 \u2022 Conference Paper<\/li>\n
  23. A. Avramidou and C. Tjortjis<\/strong>, \u201cPredicting CO2 Emissions for Buildings Using Regression and Classification\u201d, Proc. 17th IFIP Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, 2021 (Springer). \u2022 Conference Paper<\/li>\n
  24. D. P. Kasseropoulos and C. Tjortjis<\/strong>, \u201cAn Approach Utilizing Linguistic Features for Fake News Detection\u201d, Proc. 17th IFIP Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, 2021 (Springer). \u2022 Conference Paper<\/li>\n
  25. V. Sarlis, C. Tjortjis<\/strong>, \u201cSports Analytics \u2013 Evaluation of Basketball Players and Team Performance\u201d, Information Systems, Vol. 93, 2020 (Elsevier) \u2022 Journal Paper<\/li>\n
  26. Tjortjis C.,<\/strong>\u00a0\u2018Mining Association Rules from Code (MARC) to Support Legacy Software Management\u2019, Software Quality Journal. Vol. 28, no. 2, pp. 633\u2013662, 2020 (Springer) \u2022 Journal Paper<\/li>\n
  27. Rousidis D., Koukaras P.,\u00a0Tjortjis C.,<\/strong>\u00a0\u2018Social Media Prediction A Literature Review\u2019, Multimedia Tools and Applications, 79(9), 6279-6311, 2020 (Springer) \u2022 Journal Paper<\/li>\n
  28. P. Koukaras,\u00a0C. Tjortjis,<\/strong>\u00a0D. Rousidis, \u201cSocial Media Types: Introducing a Data Driven Taxonomy\u201d, Computing, Vol. 102, no. 1, pp. 295-340, 2020 (Springer) \u2022 Journal Paper<\/li>\n
  29. P. Koukaras, D. Rousidis and C. Tjortjis<\/strong>, \u201cAn Introduction to Information Network Modeling Capabilities, Utilizing Graphs\u201d, 14th Int\u2019l Conf. Metadata and Semantics Research, Vol. 1355, 134-140, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  30. D. Rousidis, P. Koukaras and C. Tjortjis<\/strong>, \u201cExamination of NoSQL Transition and Data Mining capabilities\u201d, 14th Int\u2019l Conf. Metadata and Semantics Research, Vol. 1355, 110-115, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  31. P. Koukaras, C. Berberidis, and C. Tjortjis<\/strong>, \u201cA Semi-supervised Learning Approach for Complex Information Networks\u201d, Proc. 3rd Int\u2019l Conf. Intelligent Data Communication Technologies and Internet of Things, Vol. 57, pp. 1-13, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  32. A. Mystakidis, C. Tjortjis<\/strong>, \u201cBig Data Mining for Smart Cities: Predicting Traffic Congestion using Classification\u201d, Proc. 11th IEEE Int\u2019l Conf. on Information, Intelligence, Systems and Applications, pp. 135-142, 2020 (IEEE) \u2022 Conference Paper<\/li>\n
  33. V. Chazan\u2013Pantzalis, C. Tjortjis<\/strong>, \u201cSports Analytics for Football League Table and Player Performance Prediction\u201d, Proc. 11th IEEE Int\u2019l Conf. on Information, Intelligence, Systems and Applications, pp. 73-80, 2020 (IEEE) \u2022 Conference Paper<\/li>\n
  34. D. Beleveslis, C. Tjortjis<\/strong>, \u201cPromoting Diversity in Content Based Recommendation using Feature Weighting and LSH\u201d, Proc. 16th IFIP Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, pp. 452-461, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  35. K. Christantonis, C. Tjortjis<\/strong>, A. Manos, D.E. Filippidou, \u0395. Mougiakou and E. Christelis, \u201cUsing Classification for Traffic Prediction in Smart Cities\u201d, Proc. 16th IFIP Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, pp. 52-61, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  36. E. Tsiara, C. Tjortjis<\/strong>, \u201cUsing Twitter to Predict Chart Position for Songs\u201d, Proc. 16th IFIP Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, pp. 62-72, 2020 (Springer) \u2022 Conference Paper<\/li>\n
  37. P. Koukaras, D. Rousidis,\u00a0C. Tjortjis,<\/strong>\u00a0\u201cForecasting and Prevention mechanisms using Social Media in Healthcare\u201d, Advanced Computational Intelligence Paradigms in Healthcare, 2020 (Springer) \u2022 Book chapter<\/li>\n
  38. S.M.Ghafari,\u00a0C. Tjortjis<\/strong>, \u201cA Survey on Association Rules Mining Using Heuristics\u201d, WIREs Data Mining and Knowledge Discovery, Vol. 9, no. 4, 2019 (Wiley) \u2022 Journal Paper<\/li>\n
  39. D. Beleveslis,\u00a0C. Tjortjis,<\/strong>\u00a0D. Psaradelis and D. Nikoglou, \u201cA Hybrid Method for Sentiment Analysis of Election Related Tweets\u201d, Proc. 4th IEEE SE Europe Design Automation, Computer Engineer-ing, Computer Networks, and Social Media Conf., pp. 1-7, 2019 \u2022 Conference Paper<\/li>\n
  40. D. Tasios,\u00a0C. Tjortjis,<\/strong>\u00a0A. Gregoriades, \u201cMining Traffic Accident Data for Hazard Causality Analysis\u201d Proc. 4th IEEE SE Europe Design Automation, Computer Engineering, Computer Networks, and Social Media Conf., pp. 1-7, 2019 (IEEE) \u2022 Conference Paper<\/li>\n
  41. K. Christantonis,\u00a0C. Tjortjis,<\/strong>\u00a0\u201cData Mining for Smart Cities: Predicting Electricity Consumption by Classification\u201d, Proc. 10th IEEE Int\u2019l Conf. on Information, Intelligence, Systems and Applications, pp. 67-73, 2019 (IEEE) \u2022 Conference Paper<\/li>\n
  42. K. Apostolou,\u00a0C. Tjortjis,<\/strong>\u00a0\u201cSports Analytics algorithms for performance prediction\u201d, 10th IEEE Int\u2019l Conf. on Information, Intelligence, Systems and Applications, , pp. 469-472, 2019 (IEEE) \u2022 Conference Paper<\/li>\n
  43. \u0399. Schoinas,\u00a0C. Tjortjis<\/strong>, \u201cMuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback\u201d, 15th Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, IFIP AICT 559, pp. 1\u201313, 2019 (Springer) \u2022 Conference Paper<\/li>\n
  44. P. Koukaras,\u00a0C. Tjortjis<\/strong>, \u201cSocial Media Analytics, types and methodology\u201d, Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, 2019 (Springer) \u2022 Book chapter<\/li>\n
  45. M. Koubarakis, G. Vouros, G. Chalkiadakis, V. Plagianakos,\u00a0C. Tjortjis<\/strong>, E. Kavallieratou, D. Vrakas, N. Mavridis, G. Petasis, K. Blekas, A. Krithara, \u201cAI in Greece: The Case of Research on Linked Geospatial Data\u201d, the AI magazine, Vol 39, No 2, pp. 91-96, 2018 (AAAI) \u2022 Journal Paper<\/li>\n
  46. L. Oikonomou and\u00a0C. Tjortjis<\/strong>, \u201cA Method for Predicting the Winner of the USA Presidential Elections using Data Extracted from Twitter\u201d, Proc. 3rd SEEDA Computer Engineering, Computer Networks, and Social Media Conference, 2018 (IEEE) \u2022 Conference Paper<\/li>\n
  47. Tzirakis P. and\u00a0Tjortjis C<\/strong>., \u201cT3C: Improving a Decision Tree Classification Algorithm\u2019s Interval Splits on Continuous Attributes\u201d, Advances in Data Analysis and Classification, Vol. 11, No. 2, pp. 353-370, 2017 (Springer) \u2022 Journal Paper<\/li>\n
  48. S. Yakhchi, S.M. Ghafari,\u00a0C. Tjortjis<\/strong>, M. Fazeli, \u201cARMICA-Improved: A New Approach for Association Rule Mining\u201d, LNAI, vol 10412, pp. 296-306, 2017 (Springer-Verlag) \u2022 Conference Paper<\/li>\n
  49. Nalmpantis O. and\u00a0Tjortjis C.<\/strong>, \u201cThe 50\/55 Recommender: a Method Incorporating Personality into Movie Recommender Systems\u201d, Proc. 8th Int\u2019l Conf. on Engineering Applications of Neural Networks, (CCIS) 744, pp. 1\u201310, 2017 (Springer-Verlag) \u2022 Conference Paper<\/li>\n
  50. Theodorou T.I., Salamanis A., Kehagias D., Tzovaras D., and\u00a0Tjortjis C.<\/strong>, \u201cShort-Term Traffic Prediction Under both Typical and Atypical Traffic Conditions using a Pattern Transition Model\u201d, Proc. 3rd Int\u2019l Conf. Vehicle Technology and Intelligent Transport Systems, pp. 79-89, 2017 \u2022 Conference Paper<\/li>\n
  51. Ghafari S.M. and\u00a0Tjortjis C.<\/strong>, \u201cAssociation Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA)\u201d, AICT Proc. 12th Int\u2019l Conf. on Artificial Intelligence Applications and Innovations, 2016 (Springer) \u2022 Conference Paper<\/li>\n
  52. Arshad S.,\u00a0Tjortjis C.<\/strong>, \u201cClustering Software Metric Values Extracted from C# Code for Maintainability Assessment\u201d, SETN 16, Article No. 24, Int\u2019l Conf. Proc. Series, 2016 (ACM) \u2022 Conference Paper<\/li>\n
  53. D. Papas and\u00a0C. Tjortjis<\/strong>, \u201cCombining Clustering and Classification for Software Quality Evaluation\u201d, LNAI 8445, pp. 273\u2013286, 2014 (Springer-Verlag) \u2022 Conference Paper<\/li>\n
  54. V.A. Tatsis,\u00a0C. Tjortjis<\/strong>, P. Tzirakis, \u201cEvaluating data mining algorithms using molecular dynamics trajectories\u201d, Int\u2019l Journal of Data Mining and Bioinformatics, Vol. 8, No. 2, pp. 169-187, 2013 (Inderscience) \u2022 Journal Paper<\/li>\n
  55. V.C. Gerogiannis, A. Karageorgos, L. Liu, and\u00a0C. Tjortjis<\/strong>, \u201cPersonalised Fuzzy Recommendation for High Involvement Products\u201d, Int\u2019l Conf. Systems, Man, and Cybernetics, pp. 4884-4890, 2013 (IEEE) \u2022 Conference Paper<\/li>\n
  56. Kanellopoulos Y., Antonellis P.,\u00a0Tjortjis C.<\/strong>, Makris C. and Tsirakis N., \u201ck-Attractors: A Partitional Clustering Algorithm for Numeric Data analysis\u201d, Applied Artificial Intelligence, Vol. 25, No.2, pp. 97-115, 2011 (Taylor & Francis) \u2022 Journal Paper<\/li>\n
  57. A. Karageorgos, D. Avramouli,\u00a0C. Tjortjis<\/strong>, G. Ntalos, \u201cAgent-based Digital Networking in Furniture Manufacturing Enterprises\u201d, Communications in Computer and Information Science (CCIS 88), pp. 381\u2013395, 2010 (Springer-Verlag) \u2022 Conference Paper<\/li>\n
  58. Antonellis P., Antoniou D., Kanellopoulos Y., Makris C., Theodoridis E.,\u00a0Tjortjis C.<\/strong>\u00a0and Tsirakis N., \u201cClustering for Monitoring Software Systems Maintainability Evolution\u201d, Electronic Notes in Theoretical Computer Science, Vol. 233, pp. 43-57, 2009 (Elsevier)\u2022 Journal Paper<\/li>\n
  59. Antonellis P., Antoniou D., Kanellopoulos Y., Makris C., Theodoridis E.,\u00a0Tjortjis C.<\/strong>\u00a0and Tsirakis N., \u201cCode4Thought Project: Employing the ISO\/IEC-9126 standard for Software Engineering \u2013 Product Quality Assessment\u201d, Proc. 13th European Conf. Software Maintenance and Reengineering, pp. 297-300, 2009 (IEEE) \u2022 Conference Paper<\/li>\n
  60. Denaxas S. and\u00a0Tjortjis C.<\/strong>, \u201cA GO-driven semantic similarity measure for quantifying the biological relatedness of gene products\u201d, Intelligent Decision Technologies, Vol. 3, No 4, pp. 239-248, 2009 (IOS Press,) \u2022 Journal Paper<\/li>\n
  61. Zhang S.,\u00a0Tjortjis C.<\/strong>, Zeng X., Qiao H., Buchan I. and Keane J., \u201cComparing Data Mining Methods with Logistic Regression in Childhood Obesity Prediction\u201d, Information Systems Frontiers Journal, Vol. 11, No. 4, pp. 449-460, 2009 (Springer) \u2022 Journal Paper<\/li>\n
  62. Denaxas S. and\u00a0Tjortjis C.<\/strong>, \u201cScoring and summarizing gene product clusters using the Gene Ontology\u201d, Int\u2019l Journal of Data Mining and Bioinformatics, Vol. 2, No. 3, pp.216- 235, 2008 (Inderscience) \u2022 Journal Paper<\/li>\n
  63. Kanellopoulos Y., Heitlager I.,\u00a0Tjortjis C.<\/strong>, and Visser J., \u201cInterpretation of Source Code Clusters in Terms of the ISO\/IEC-9126 Maintainability Characteristics\u201d, Proc. 12th European Conf. Software Maintenance and Reengineering, pp. 63-72, 2008 (IEEE) \u2022 Conference Paper<\/li>\n
  64. Kanellopoulos Y., Antonellis P.,\u00a0Tjortjis C.<\/strong>\u00a0and Makris C., \u201ck-Attractors: A Clustering Algorithm for Software Measurement Data Analysis\u201d, Proc. 19th Int\u2019l Conf. on Tools with Artificial Intelligence, pp. 358-365, 2007 (IEEE) \u2022 Conference Paper<\/li>\n
  65. Kanellopoulos Y., Makris C. and\u00a0Tjortjis C.<\/strong>, \u201cAn Improved Methodology on Information Distillation by Mining Program Source Code\u201d, Data & Knowledge Engineering, Vol. 61, No 2, pp. 359-383, 2007 (Elsevier) \u2022 Journal Paper<\/li>\n
  66. Tjortjis C.<\/strong>\u00a0and Wang C., \u201cHybridSet: An Effective Approach to Association Rule Mining\u201d, Proc. 2007 European Conference on Operational Research\u00a0 2007 \u2022 Conference Paper<\/li>\n
  67. Tjortjis C.<\/strong>, Saraee M., Theodoulidis B. and Keane J.A., \u201cUsing T3, an Improved Decision Tree Classifier, for Mining Stroke Related Medical Data\u201d, Methods of Information in Medicine, Vol. 46, No. 5, pp. 523-529, 2007 (Schattauer GmbH) \u2022 Journal Paper<\/li>\n
  68. Denaxas S. and\u00a0Tjortjis C.<\/strong>, \u201cQuantifying the Biological Similarity between Gene Products Using GO: An Application of the Vector Space Model\u201d, Proc. Information Technology in Biomedicine, 2006 (IEEE) \u2022 Conference Paper<\/li>\n
  69. Kanellopoulos Y., Dimopoulos T.,\u00a0Tjortjis C.<\/strong>\u00a0and Makris C., \u201cMining Source Code Elements for Comprehending Object-Oriented Systems and Evaluating Their Maintainability\u201d, SIGKDD Explorations, Vol. 8, No. 1, pp. 33-40, 2006 (ACM Press) \u2022 Journal Paper<\/li>\n
  70. Marchant J.,\u00a0Tjortjis C.<\/strong>, and Turega M., \u201cA Metric of Confidence in Requirements Gathered from Legacy Systems: Two Industrial Case Studies\u201d, Proc. 10th European Conf. Software Maintenance and Reengineering, pp. 353-359, 2006 (IEEE) \u2022 Conference Paper<\/li>\n
  71. Muyeba M., Khan M., Malik, Z. and\u00a0Tjortjis C.<\/strong>, \u201cTowards Healthy Association Rule Mining (HARM): A Fuzzy Quantitative Approach\u201d, LNCS, Vol. 4224, pp. 1014-1022, 2006 (Springer-Verlag) \u2022 Conference Paper<\/li>\n
  72. Rousidis D. and\u00a0Tjortjis C.<\/strong>, \u201cClustering data retrieved from Java source code to support software maintenance: A case study\u201d, Proc. 9th European Conf. Software Maintenance and Reengineering, pp. 276-279, 2005 (IEEE) \u2022 Conference Paper<\/li>\n
  73. Kanellopoulos Y. and\u00a0Tjortjis C.<\/strong>, \u201cData Mining Source Code to Facilitate Program Comprehension: Experiments on Clustering Data Retrieved from C++ Programs\u201d, Proc. 12th Int\u2019l Workshop Program Comprehension, pp. 214-223, 2004 (IEEE) \u2022 Conference Paper<\/li>\n
  74. Wang C. and\u00a0Tjortjis C.<\/strong>, \u201cPRICES: An Efficient Algorithm for Mining Association Rules\u201d, Lecture Notes in Computer Science, Vol. 3177, pp. 352-358, 2004 (Springer-Verlag,) \u2022 Conference Paper<\/li>\n
  75. Dong L. and\u00a0Tjortjis C.<\/strong>, \u201cExperiences of Using a Quantitative Approach for Mining Association Rules\u201d, LNCS, Vol. 2690, pp. 693-700, 2003 (Springer-Verlag,) \u2022 Conference Paper<\/li>\n
  76. Tjortjis C.<\/strong>, Sinos L. and Layzell P.J., \u201cFacilitating Program Comprehension by Mining Association Rules from Source Code\u201d, Proc. 11th Int\u2019l Workshop Program Comprehension, pp. 125-132, 2003 (IEEE) \u2022 Conference Paper<\/li>\n
  77. Tjortjis C.<\/strong>\u00a0and Keane J.A, \u201cT3: an Improved Classification Algorithm for Data Mining\u201d, LNCS, Vol. 2412, pp. 50-55, 2002 (Springer-Verlag,) \u2022 Conference Paper<\/li>\n
  78. Tjortjis C<\/strong>., Dafoulas G., Layzell P.J., Macaulay L., \u201cA Model for Selecting CSCW Technologies for Distributed Software Maintenance Teams in Virtual Organisations\u201d, Proc. 26th Int\u2019l Computer Software Applications Conf., pp. 1104-1108, 2002 (IEEE) \u2022 Conference Paper<\/li>\n
  79. Tjortjis C.<\/strong>, Gold N., Layzell P.J. and Bennett K., \u201cFrom System Comprehension to Program Comprehension\u201d, Proc. 26th Int\u2019l Computer Software Applications Conf., pp. 427-432, 2002 (IEEE) \u2022 Conference Paper<\/li>\n
  80. Tjortjis C.<\/strong>\u00a0and Layzell P.J., \u201cExpert Maintainers\u2019 Strategies and Needs when Understanding Software: A Qualitative Empirical Study\u201d, Proc. 8th Asia-Pacific Software Engineering Conf, pp. 281-287, 2001 (IEEE) \u2022 Conference Paper<\/li>\n<\/ol>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":3268,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-4096","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/pages\/4096","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/comments?post=4096"}],"version-history":[{"count":4,"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/pages\/4096\/revisions"}],"predecessor-version":[{"id":7649,"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/pages\/4096\/revisions\/7649"}],"up":[{"embeddable":true,"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/pages\/3268"}],"wp:attachment":[{"href":"https:\/\/www.ihu.gr\/ucips\/wp-json\/wp\/v2\/media?parent=4096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}