• Data guided approach to generate multi-dimensional schema for targeted knowledge discovery

      Pears, RL; Usman, M; Fong, A; Usman, M; Pears, RL; Fong, A (Australian Computer Society (ACS), 2012)
      Data mining and data warehousing are two key technologies which have made significant contributions to the field of knowledge discovery in a variety of domains. More recently, the integrated use of traditional data mining ...
    • HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values

      Koh, YS; Pears, RL (Springer, 2015)
      We propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning ...
    • One Pass Concept Change Detection for Data Streams

      Sakthithasan, S; Pears, RL; Koh, YS (Springer Verlag, 2013)
      In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as models generated need to be updated when significant changes in the ...
    • Precise guidance to dynamic test generation

      Pears, RL; Fong, A; Do, T; Do, T; Fong, A; Pears, RL (DBLP, 2012)
      Dynamic symbolic execution has been shown an effective technique for automated test input generation. However, its scalability is limited due to the combinatorial explosion of the path space. We propose to take advantage ...
    • Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams

      Sripirikas, S; Pears, RL (IEEE, 2015)
      In this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment. Previous research showed that compact versions of Decision Trees can be obtained by ...