• A (k, p)-anonymity Framework to Sanitize Transactional Database with Personalized Sensitivity 

      Zhang, Binbin; Lin, Chun Wei; Liu, Qiankun; Fournier-Viger, Philippe; Djenouri, Youcef (Journal article; Peer reviewed, 2019)
      In recent years, analyzing transactional data has become an important data analytic task since it can discover important information in several domains, for recommendation, prediction, and personalization. Nonetheless, ...
    • A Sanitization Approach to Secure Shared Data in an IoT Environment 

      Lin, Chun Wei; Wu, Jimmy Ming-Tai; Fournier-Viger, Philippe; Djenouri, Youcef; Chen, Chun-Hao; Zhang, Yuyu (Journal article; Peer reviewed, 2019)
      Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention. During shared or exchanged ...
    • A Survey on Urban Traffic Anomalies Detection Algorithms 

      Djenouri, Youcef; Belhadi, Asma; Lin, Chun Wei; Djenouri, Djamel; Cano, Alberto (Journal article; Peer reviewed, 2019)
      This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups ...
    • Adapted k-Nearest Neighbors for Detecting Anomalies on Spatio-Temporal Traffic Flow 

      Djenouri, Youcef; Belhadi, Asma; Lin, Chun Wei; Djenouri, Djamel; Cano, Alberto (Journal article; Peer reviewed, 2019)
      Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances ...
    • Exploring Pattern Mining for Solving the Ontology Matching Problem 

      Belhadi, Hiba; Akli-Astouati, Karima; Djenouri, Youcef; Lin, Chun Wei (Peer reviewed; Journal article, 2019)
      This paper deals with the ontology matching problem, and proposes a pattern mining approach that exploits the different correlation and dependencies between the different properties to select the most appropriate features ...
    • GFSOM: Genetic Feature Selection for Ontology Matching 

      Belhadi, Hiba; Akli-Astouati, Karima; Djenouri, Youcef; Lin, Chun Wei; Wu, Jimmy Ming-Tai (Journal article; Peer reviewed, 2019)
      This paper studies the ontology matching problem and proposes a genetic feature selection approach for ontology matching (GFSOM), which exploits the feature selection using the genetic approach to select the most appropriate ...
    • Highly Efficient Pattern Mining Based on Transaction Decomposition 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri (Chapter; Peer reviewed, 2019)
      This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction ...
    • Machine Learning for Smart Building Applications: Review and Taxonomy 

      Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko (Journal article; Peer reviewed, 2019)
      The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions ...
    • Space-time series clustering: Algorithms, taxonomy, and case study on urban smart cities 

      Belhadi, Asma; Djenouri, Youcef; Nørvåg, Kjetil; Ramampiaro, Heri; Masseglia, Florent; Lin, Jerry Chun-Wei (Journal article; Peer reviewed, 2020)
      This paper provides a short overview of space–time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clustering. The first hierarchical ...
    • The density-based clustering method for privacy-preserving data mining 

      Wu, Jimmy Ming-Tai; Lin, Chun Wei; Fournier-Viger, Philippe; Djenouri, Youcef; Chen, Chun-Hao; Li, Zhongcui (Journal article; Peer reviewed, 2019)
      Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving ...