DataBase and Data Mining Group

Paolo Garza

Paolo Garza, Associate professor

I have been an associate professor at the Dipartimento di Automatica e Informatica, Politecnico di Torino since December 2018. Prior to that, I spent three years as an assistant professor at Politecnico di Milano. I received the master’s and Ph.D. degrees in computer engineering from Politecnico di Torino.
I coauthored about 100 papers in the areas of data mining and machine learning. My research interests are in the fields of data mining, database systems, and big data analytics.

Research Interests

Big Data, Data Mining, Associative Classification, Textual Data Summarization, Itemset Mining, Clustering.

  • Big Data
    • Matteo Corain, Paolo Garza, Abolfazl Asudeh: DBSCOUT: A Density-based Method for Scalable Outlier Detection in Very Large Datasets. ICDE 2021: 37-48 (2021) (link)
    • Daniele Apiletti, Elena Baralis, Tania Cerquitelli, Paolo Garza, Fabio Pulvirenti, Pietro Michiardi: A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data. Big Data Res. 10: 53-69 (2017) (link)
  • Textual Data Summarization
    • Luca Cagliero, Paolo Garza, Elena Baralis: ELSA: A Multilingual Document Summarization Algorithm Based on Frequent Itemsets and Latent Semantic Analysis. ACM Trans. Inf. Syst. 37(2): 21:1-21:33 (2019) (link)
    • Elena Baralis, Luca Cagliero, Alessandro Fiori, Paolo Garza: MWI-Sum: A Multilingual Summarizer Based on Frequent Weighted Itemsets. ACM Trans. Inf. Syst. 34(1): 5:1-5:35 (2015) (link)
  • Associative Classification
    • Elena Baralis, Luca Cagliero, Paolo Garza: EnBay: A Novel Pattern-Based Bayesian Classifier. IEEE Trans. Knowl. Data Eng. 25(12): 2780-2795 (2013) (link)
    • Elena Baralis, Silvia Chiusano, Paolo Garza: A Lazy Approach to Associative Classification. IEEE Trans. Knowl. Data Eng. 20(2): 156-171 (2008) (link)
    • Elena Baralis, Paolo Garza: Majority Classification by Means of Association Rules. PKDD 2003: 35-46 (2003) (link)
    • Elena Baralis, Paolo Garza: A Lazy Approach to Pruning Classification Rules. ICDM 2002: 35-42 (2002) (link)
  • Itemset Mining
    • Luca Cagliero, Paolo Garza: Infrequent Weighted Itemset Mining Using Frequent Pattern Growth. IEEE Trans. Knowl. Data Eng. 26(4): 903-915 (2014) (link)
    • Elena Baralis, Luca Cagliero, Tania Cerquitelli, Paolo Garza: Generalized association rule mining with constraints. Inf. Sci. 194: 68-84 (2012) (link)
  • Principal investigator/Co-Principal investigator
    • Machine learning for networksupervision and fault management, (2021-2021) – Funded by TIM S.P.A. – Principal investigator
    • ML4QoE (Machine Learning for QoE): re-enabling QoE for multiparty real time, (2019-2022) – Funded by Cisco Systems Inc. (USA) – Co-Principal investigator
  • Participant
    • (I-REACT) Improving Resilience to Emergencies through Advanced Cyber Technologies (2016-2019) – H2020 European project – Funded by the European Community – Data Protection Officer and Privacy and Security Manager, Task Leader
    • (ONTIC) Online Traffic Network Characterization (2014-2017) – FP7 Eupoean project – Funded by the European Community