• 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 ...
    • 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 ...
    • 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 ...
    • 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 ...