Frequent itemsets mining is well explored for various data types, and its computational complexity is well understood. Based on our previous work by Nguyen and Orlowska (2005), th...
Subspace clustering and frequent itemset mining via “stepby-step” algorithms that search the subspace/pattern lattice in a top-down or bottom-up fashion do not scale to large ...
Most current ontology management systems concentrate on detecting usage-driven changes and representing changes formally in order to maintain the consistency. In this paper, we pr...
Majigsuren Enkhsaikhan, Wilson Wong, Wei Liu, Mark...
Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adap...
Clifton Phua, Kate Smith-Miles, Vincent C. S. Lee,...
Knowledge discovery allows considerable insight into data. This brings with it the inherent risk that what is inferred may be private or ethically sensitive. The process of genera...