• Non-redundant rare itemset generation

      Koh, YS; Pears, R (Australian Computer Society (ACS), 2009)
      Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has ...
    • Ontologies and machine learning systems

      Tegginmath, S; Pears, R; Kasabov, N (Springer, 2014)
      In this chapter we review the uses of ontologies within bioinformatics and neuroinformatics and the various attempts to combine machine learning (ML) and ontologies, and the uses of data mining ontologies. This is a diverse ...
    • Personalised modelling for multiple time-series data prediction

      Widiputra, H; Pears, R; Kasabov, N (Springer-Verlag, 2008)
      The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of ...
    • Rare association rule mining via transaction clustering

      Koh, YS; Pears, R (Australian Computer Society (ACS), 2008)
      Rare association rule mining has received a great deal of attention in the recent past. In this research, we use transaction clustering as a pre-processing mechanism to generate rare association rules. The basic concept ...
    • Synthetic Minority Over-sampling TEchnique (SMOTE) for predicting software build outcomes

      Pears, R; Finlay, JA; Connor, AM (Knowledge Systems Institute Graduate School, 2014)
      In this research we use a data stream approach to mining data and construct decision Tree models that predict software build outcomes in terms of software metrics that are derived from source code used in the software ...