The Data Mining and Analytics research group (DaMA)

The Data Mining and Analytics research group was set up in 2016 by As. Prof. Christos Tjortjis at the School of Science and Technology, International Hellenic University.

Initially, it comprised 10 members, i.e. 2 PhD students: S.M. Ghafari (Association rule mining) and S. Yakhchi, (Big data mining), 5 MSc students: O. Nalmpantis (Movie Recommender System), L. Oikonomou (Mining Twitter data to Predict the USA 2016 election winner) I. Papikas (Human Rating System), and T.-I. Theodorou (Traffic Prediction Techniques under Abnormal Traffic Conditions), as well as two associated members: Prof. G. Evangelidis (Dean of the School) and Dr Christos Berberidis (Data mining expert).

Group alumni include: C. Charisiadis (Data Mining on Source Code), D. Kalliantasis (Data mining for evaluating startups and forecasting stock fluctuations), L. Konstantopoulos (An automated tool for evaluating social media influencers), A. Paraskevopoulos (Product recommendation system), F. Touparis (Predicting stocks movement using social media analytics), K. Apostolou (Sports Analytics algorithms for performance prediction), K. Christantonis (Data mining for smart cities), G. Giaglis (Programmatic Automation & Yield Optimization on the Ad Exchange), I. Schoinas (Product Recommendation system), D. Tasios (Mining Traffic Data), V. Tsarapatsanis (Fake news detection), E. Tsiara (Forecasting with predictive social media analytics) and D. Beleveslis (Heuristic Approach for Content Based Recommendation System Based on Feature Weighting and LSH), V. Chazan-Pantzalis (Sports Analytics Algorithms for Performance Prediction), A. Mystakidis (Data mining for smart cities), I. Nasiara (The Impact of Twitter Sentiment on Ryanair's Business Performance), V. Tsichli (Predicting Stock Market Movements Using Social Media and Machine Learning), Y. Al-Dara (Data Analytics for the Automatic Generation of Electricity Usage Recommendations), A. Avramidou (Building CO2 emissions prediction using Machine Learning/ Data mining), P. Belogianni (Recommendation systems), C. Kaimakamis (Sports Analytics algorithms for performance prediction), D.P. Kasseropoulos (Influencer/ fake news detection in social media), P. Koudoumas (Sports Analytics algorithms for performance prediction), S. Liapis (Big Data mining for smart cities), C. Nousi (Stock market prediction using data mining), V. Papanikolaou (Data mining for smart cities: Predicting energy consumption in public buildings), B.S. Syuqran Naim (Big Data mining for smart cities), O. Trasanidis (Decision making tool for smart cities), G. Asderis (Sentiment analysis on twitter data), E. Kapoteli (Sentiment Analysis Related to COVID-19 Vaccines), M. Karagkiozidou (Sentiment analysis on twitter data), G. Papageorgiou (Sports Analytics algorithms for performance prediction), I. Tourpeslis (Data mining for smart cities), M. Vlachos-Giovanopoulos (Forecasting with predictive social media analytics), V. Chouliara (Fake News Detection), C. Dontaki (Sentiment Analysis on English and Greek Twitter Data towards vaccinations), O. Geromichalou (Traffic Prediction), N. Giannakoulas (Sports Analytics Performance Prediction), F. Shaban (Electricity consumption prediction using data mining), N. Tsalikidis (Data Mining/ML for Smart House infrastructure), and M. Vasileiou (Data mining software management).

Currently, it comprises 13 members, including one Adjunct Lecturer, Dr P. Koukaras (PhD in Interdisciplinary data science methods using machine learning for enhanced knowledge acquisition), one postdoctoral researcher Dr V. Sarlis (PhD in Data Science for Sports Analytics), 5 PhD students: A. Kousis (Data Science for Smart Cities), A. Mystakidis (Data Science for Smart Cities), D. Rousidis (Forecasting with Social media data), G. Papageorgiou (Data Science for Business Applications), and N. Stasinos (Data Science for Law), as well as 5 MSc students: D. Gerakas (Basketball Analytics for Prediction of Performance during the last minutes of a game), D. Gkaimanis (Stock Market Prediction using Double-DQN and Sentiment Analysis), P. Karatakis (Data Mining for Software Management: Automatic marking of complex Rust code using software metrics), C. Markopoulou (Sport Analytics Algorithms for Football Performance Prediction),and K.V. Tompra (Enhancing preventive healthcare: Identi-fying high-risk patients for cardiovascular diseases).

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Our research focuses on three main themes:

A.     Algorithms

Including Association Rule Mining (ARMICA and ARMICA-Improved), Decision tree classification (T3, T3C) and Clustering.

B.     Analytics

Social media analytics, Sports analytics

C.      Applications

Including Healthcare, Energy, Software Quality, Smart Cities and Recommendation Systems.

 

Key publications

*      A. Mystakidis, P. Koukaras, N. Tsalikidis, D. Ioannidis and C. Tjortjis, 'Energy Forecasting: A Comprehensive Review of Techniques and Technologies', Energies, 17, 1662, 2024, (MDPI).

*     P. Koukaras, A. Mustapha, A. Mystakidis, and C. Tjortjis, 'Optimizing Building Short-Term Load Forecasting A Comparative Analysis of Machine Learning Models', Energies, 17, 1450, 2024, (MDPI).

*     G. Papageorgiou, V. Sarlis, C. Tjortjis, 'Evaluating the Effectiveness of Machine Learning Models for Performance Forecasting in Basketball: A Comparative Study', Knowledge and Information Systems, 2024, (Springer).

*     G. Papageorgiou, V. Sarlis, C. Tjortjis, 'An Innovative Method for Accurate NBA Player Performance Forecasting and Line-up Optimization in Daily Fantasy Sports', Int’l Journal of Data Science and Analytics, 2024, (Springer).

*     A. Kousis, C. Tjortjis, 'Investigating the key aspects of a smart city through topic modeling and thematic analysis', Future Internet, Vol. 16, No. 1: 3. 2024, (MDPI).

*         N. Tsalikidis, A. Mystakidis, P. Koukaras, M. Ivaskevicius, L. Morkunaite, D. Ioannidis, P.A. Fokaides, C. Tjortjis, D. Tzovaras, 'Urban traffic congestion prediction A multi-step approach utilizing sensor data and weather information', Smart Cities, Vol. 7, No. 1, pp. 233–253. 2024, (MDPI).

*         N. Tsalikidis, A. Mystakidis, C. Tjortjis, P. Koukaras, D. Ioannidis, 'Energy Load Forecasting: One-Step Ahead Hybrid Model utilizing ensembling', Computing, 2024, (Springer)..

*         P. Koukaras, K. Afentoulis, P. Gkaidatzis, A. Mystakidis, D. Ioannidis, S. Vagropoulos and C. Tjortjis. 'Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions', Energies, 2024, (MDPI).

*         G. Papageorgiou, V. Sarlis, C. Tjortjis, 'Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts', Information, Vol. 15, No. 1, 61, 2024, (MDPI).

*         V. Sarlis, G. Papageorgiou, C. Tjortjis, 'Injury Patterns and Impact on Performance in the NBA League using Sports Analytics', Computation, Vol. 12, No. 2, 36. 2024, (MDPI).

*         A. Mystakidis, E. Ntozi, K. Afentoulis, P. Koukaras, P. Gkaidatzis, D. Ioannidis, C. Tjortjis and D. Tzovaras, 'Energy generation forecasting: Elevating performance with machine and deep learning', Computing, Vol. 105, pp. 1623–1645, 2023, (Springer).

*         P. Koukaras, D. Rousidis and C. Tjortjis, 'Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis Towards COVID-19 Cases and Deaths', Informatics, Vol. 10, No. 4, 88, 2023, (MDPI).

*         V. Sarlis, G. Papageorgiou, C. Tjortjis, 'Sports Analytics and Text Mining NBA Data to Assess Recovery from Injuries and their Economic Impact', Computers, Vol. 12, No. 12, 261, 2023, (MDPI).

*         N. Tsalikidis, A. Mystakidis, C. Tjortjis, P. Koukaras, D. Ioannidis, “Energy Load Forecasting: One-Step Ahead Hybrid Model utilizing ensembling”, Computing, Springer, 2023.

*         F. Shaban, P. Siskos and C. Tjortjis, “Electromobility prospects in Greece by 2030: a regional perspective on strategic policy analysis”, Energies, Vol. 16, No. 16, 6083, MDPI, 2023

*         M.T. Siddique, P. Koukaras, D. Ioannidis, C. Tjortjis, “A Methodology Integrating Quantitative As-sessment of Energy Efficient Operation and Occupant needs into the Smart Readiness Indicator”, Energies, , Vol. 16, No. 19, 7007; MDPI, 2023.

*         M.T. Siddique, P. Koukaras, D. Ioannidis, C. Tjortjis, “SmartBuild RecSys: A Recommendation System based on the Smart Readiness Indicator for Energy Efficiency in Buildings”, Algorithms, Vol. 16, No. 10, 482, MDPI, 2023,

*         A. Mystakidis, N. Tsalikidis, P. Koukaras, C. Kontoulis, P.A. Gkaidatzis, D. Ioannidis, C. Tjortjis, and D. Tzovaras, “Power Load Forecasting: A Time-series Multi-step ahead and Multi-model analysis”, Proc. IEEE 58th Int’l Universities Power Engineering Conf. (UPEC 23), 2023.

*         C. Dontaki, P. Koukaras, and C. Tjortjis, “Sentiment Analysis on English and Greek Twitter Data regarding Vaccinations”, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2023.

*         M. Vasileiou, G. Papageorgiou, C. Tjortjis, “A Machine Learning Approach for Effective Software Defect Detection”, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2023 .

*         A. Mystakidis, O. Geromichalou, C. Tjortjis, “Data Mining for Smart Cities: Traffic Congestion Prediction”, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2024

*       A. Mystakidis, E. Ntozi, K. Afentoulis, P. Koukaras, P. Gkaidatzis, D. Ioannidis, C. Tjortjis and D. Tzovaras, “Energy generation forecasting: Elevating performance with machine and deep learning”, Computing, Vol. 105, pp. 1623–1645, 2023

*       N. Stasinos, A. Kousis, V. Sarlis, A. Mystakidis, D. Rousidis, P. Koukaras, I. Kotsiopoulos, C. Tjortjis, “A Tri-model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study”, Algorithms, Vol. 16, No. 3: 140 (MDPI), 2023.

*        V. Chouliara, P. Koukaras and C. Tjortjis, “Fake News Detection utilizing textual cues” Proc. 19th Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI 23).

*       N. Giannakoulas, G. Papageorgiou, C. Tjortjis, “Forecasting Goal Performance for Top League Football Players: A Comparative Study” Proc. 19th Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI 23).

*      D. P. Kasseropoulos, P. Koukaras and C. Tjortjis, “Exploiting textual information for fake news detection”, Int’l Journal of Neural Systems, Vol. 32, No. 12, World Scientific Publishing, 2022,

*        P. Koukaras, C. Tjortjis and D. Rousidis, “Mining Association Rules from COVID-19 Related Twitter Data to Discover Word Patterns, Topics and Inferences”, Information Systems, p. 102054, Elsevier, 2022.

*        P. Koukaras, C. Tjortjis, P. Gkaidatzis, N. Bezas, D. Ioannidis, and D. Tzovaras, “A Multidisciplinary Approach on Efficient Virtual Microgrid to Virtual Microgrid Energy Balancing Incorporating Data Preprocessing Techniques”, Computing,, Vol. 104, No. 1, pp. 209-250, 2022.

*        P. Koukaras, C. Nousi and C. Tjortjis, “Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning”, Telecom, MDPI, 3(2), 358-378, 2022.

*         Ε. Kapoteli, P. Koukaras, C. Tjortjis, Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case studies in English and Greek language, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

*         M. Karagkiozidou, P. Koukaras, C. Tjortjis, Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

*         P. Koukaras, A. Dimara, S. Herrera, N. Zangrando, S. Krinidis, D. Ioannidis, P. Fraternali, C. Tjortjis, C.-N. Anagnostopoulos, D. Tzovaras, Proactive buildings: A prescriptive maintenance approach, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

*         A. Ahmed, C. Tjortjis, “Machine Learning based IoT-BotNet Attack Detection Using Real-time Heterogeneous Data”, 2nd Int’l Conf. on Electrical, Computer and Energy Technologies (ICECET 22).

*         M. Vlachos Giovanopoulos, G. Michailidis, P. Koukaras and C. Tjortjis,“Healthcare support using Data Mining: A case study on stroke prediction”, Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library Vol. 229, pp. 71-93, Springer, 2023.

*         V. Chouliara, E. Kapoteli, P. Koukaras and C. Tjortjis, “Social Media Sentiment Analysis related to COVID-19 Vaccinations”, Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library Vol. 229, pp. 47–69, Springer, 2023.

*         C. Nousi, P. Belogianni, P. Koukaras and C. Tjortjis, “Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications”, Handbook of Artificial Intelligence in Healthcare, Intelligent Systems Reference Library, vol 211, pp. 287-312, Springer, 2022

*         T. Chatzinikolaou, E. Vogiatzi, A. Kousis, C. Tjortjis, “Smart Healthcare Support Using Data Mining and Machine Learning”, IoT and WSN based Smart Cities: A Machine Learning Perspective, EAI/Springer Innovations in Communication and Computing, 2022.

*S. Liapis, K. Christantonis, V. Chazan-Pantzalis, A. Manos, D.E. Filippidou, and C. Tjortjis, “A methodology using classification for traffic prediction: Featuring the impact of COVID-19”, Integrated Computer-Aided Engineering (ICAE), ), Vol. 28, pp. 417-435, IOS Press, 2021.

*         V. Sarlis, V. Chatziilias, C. Tjortjis, D. Mandalidis, 'A Data Science Approach Analysing the Impact of Injuries on Basketball Players and Team Performance', Information Systems, Elsevier, 2021.

*        P. Koukaras, N. Bezas, P. Gkaidatzis, D. Ioannidis, D. Tzovaras, and C. Tjortjis, 'Introducing a Novel Approach in One-step Ahead Energy Load Forecasting', Sustainable Computing: Informatics and Systems, Elsevier, 2021.

*         P. Koukaras, P. Gkaidatzis, N. Bezas, T. Bragatto, M. Antal, F. Carere, D. Ioannidis, C. Tjortjis and D. Tzovaras, “A Tri-layer Optimization Framework for One-day Ahead Energy Scheduling based on Cost and Discomfort Minimization”, Energies, Vol. 14, no 12, 3599; MDPI, 2021.

*         A. Kousis and C. Tjortjis, ' Data Mining Algorithms for Smart Cities: A Bibliometric Analysis', Algorithms, Vol. 14, no. 8, 242, MDPI, 2021.

*         A. Mystakidis, N. Stasinos, A. Kousis, V. Sarlis, P. Koukaras, D. Rousidis, I. Kotsiopoulos, C. Tjortjis, ' Predicting Covid-19 ICU needs using Deep Learning, XGBoost and Random Forest Regression with the Sliding Window technique', IEEE Smart Cities, July 2021.

*         P. Koukaras, D. Rousidis and C. Tjortjis, 'Introducing a novel Bi-functional method for Exploiting Sentiment in Complex Information Networks', Int’l Journal of Metadata, Semantics and Ontologies. Inderscience, 2021.

*         A. Avramidou and C. Tjortjis, 'Predicting CO2 Emissions for Buildings Using Regression and Classification', Proc. 17th IFIP Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 21).

*         D. P. Kasseropoulos and C. Tjortjis, 'An Approach Utilizing Linguistic Features for Fake News Detection', Proc. 17th IFIP Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 21)

*         C. Nousi and C. Tjortjis, 'A Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and StockTwits Data', Proc. 6th IEEE South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 21), 2021.

*         P. Koukaras, V. Tsichli, and C. Tjortjis, 'Predicting Stock Market Movements with Social Media and Machine Learning', Proc. 17th Int’l Conf. on Web Information Systems and Technologies (WEBIST 21), 2021

*         Tjortjis C., 'Mining Association Rules from Code (MARC) to Support Legacy Software Management' , Software Quality Journal, Springer, Vol. 28, no. 2, pp. 633-662 June 2020.

*         V. Sarlis, C. Tjortjis, 'Sports Analytics - Evaluation of Basketball Players and Team Performance', Information Systems, Elsevier, Vol. 93, November 2020.

*         Rousidis D., Koukaras P., Tjortjis C., 'Social Media Prediction A Literature Review', Multimedia Tools and Applications, Springer, 2020.

*         Koukaras P., Tjortjis C., Rousidis D., 'Social Media Types: Introducing a Data Driven Taxonomy', Computing, Vol. 102, no. 1, pp. 295-340, Springer, 2020.

*         K. Christantonis, C. Tjortjis, A. Manos, D Filippidou and E. Christelis, 'Smart Cities Data Classification for Electricity Consumption & Traffic Prediction', Automatics & Software Enginery, 31(1), 2020.

*         P. Koukaras, C. Berberidis, and C. Tjortjis, 'A Semi-supervised Learning Approach for Complex Information Networks', 3rd Int'l Conf. Intelligent Data Communication Technologies and Internet of Things (ICICI 2020).

*         P. Koukaras, D. Rousidis and C. Tjortjis, 'An Introduction to Information Network Modeling Capabilities, Utilizing Graphs', 14th Int'l Conf. Metadata and Semantics Research (MTSR2020), Communications in Computer & Information Science (CCIS), Springer, 2020.

*         D. Rousidis, P. Koukaras and C. Tjortjis, 'Examination of NoSQL Transition and Data Mining capabilities', 14th Int'l Conf. Metadata and Semantics Research (MTSR2020), Communications in Computer & Information Science (CCIS), Springer, 2020.

*         A. Mystakidis, C. Tjortjis, 'Big Data Mining for Smart Cities: Predicting Traffic Congestion using Classification', Proc. 11th IEEE Int'l Conf. on Information, Intelligence, Systems & Applications (IISA 20).

*         V. Chazan-Pantzalis, C. Tjortjis, 'Sports Analytics for Football League Table and Player Performance Prediction', Proc. 11th IEEE Int'l Conf. on Information, Intelligence, Systems and Applications (IISA 20).

*         D. Beleveslis, C. Tjortjis, 'Promoting Diversity in Content Based Recommendation using Feature Weighting and LSH', 16th Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 20).

*         K. Christantonis, C. Tjortjis, A. Manos, D.E. Filippidou, E. Mougiakou and E. Christelis, 'Using Classification for Traffic Prediction in Smart Cities', 16th Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 20).

*         E. Tsiara, C. Tjortjis, 'Using Twitter to Predict Chart Position for Songs', 16th Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 20).

*         Ghafari, S.M.; Tjortjis, C. 'A Survey on Association Rules Mining Using Heuristics', WIREs Data Mining and Knowledge Discovery, Vol. 9, no. 4, Wiley, 2019.

*        Christantonis K., Tjortjis C., 'Data Mining for Smart Cities: Predicting Electricity Consumption by Classification', IEEE 10th Int'l Conf. on Information, Intelligence, Systems and Applications (IISA 2019), 2019.

*         Apostolou K., Tjortjis C., 'Sports Analytics algorithms for performance prediction', IEEE 10th Int'l Conf. on Information, Intelligence, Systems and Applications (IISA 2019), 2019.

*         Schoinas I., Tjortjis C., 'MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback', 15th Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 19) Springer, 2019.

*         Beleveslis D., Tjortjis C., Psaradelis D. and Nikoglou D., 'A Hybrid Method for Sentiment Analysis of Election Related Tweets', 4th IEEE SE Europe Design Automation, Computer Engineering, Computer Networks, and Social Media Conf. (SEEDA-CECNSM) 2019.

*         Tasios D., Tjortjis C., Gregoriades A., 'Mining Traffic Accident Data for Hazard Causality Analysis', 4th IEEE SE Europe Design Automation, Computer Engineering, Computer Networks, and Social Media Conf. (SEEDA-CECNSM) 2019.

*         Koukaras P., Tjortjis C., 'Social Media Analytics, types and methodology', Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, pp. 401-427,Springer, 2019.

*         Oikonomou L. and Tjortjis C., 'A Method for Predicting the Winner of the USA Presidential Elections using Data Extracted from Twitter', 3rd IEEE SE Europe Design Automation, Computer Engineering, Computer Networks, and Social Media Conf. (IEEE SEEDA-CECNSM18), 2018.

*         Tzirakis P. and Tjortjis C., 'T3C: Improving a Decision Tree Classification Algorithm's Interval Splits on Continuous Attributes', Advances in Data Analysis and Classification, Vol. 11, No. 2, pp. 353-370, Springer, 2017.

*         Yakhchi S., Ghafari S.M., Tjortjis C., Fazeli M., 'ARMICA-Improved: A New Approach for Association Rule Mining', Proc. 10th Int'l Conf. on Knowledge Science, Engineering and Management (KSEM 17), Lecture Notes in Artificial Indigence, vol 10412, pp. 296-306, Springer-Verlag, 2017.

*         Nalmpantis O. and Tjortjis C., 'The 50/50 Recommender: a Method Incorporating Personality into Movie Recommender Systems', Proc. 18th Int'l Conf. on Engineering Applications of Neural Networks (EANN 17), Springer CCIS 'Communications in Computer and Information Science', pp. 498-507, 2017.

*         Arshad S., Tjortjis C. 'Clustering Software Metric Values Extracted from C# Code for Maintainability Assessment', Article No. 24, ACM Int'l Conf. Proc. Series, 2016.

*         Ghafari S.M. and Tjortjis C., 'Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA) ', IFIP AICT Proc. 12th Int'l Conf. on Artificial Intelligence Applications and Innovations (AIAI 2016). Springer, 2016.