In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new par...
The exceptional growth of graphics hardware in programmability and data processing speed in the past few years has fuelled extensive research in using it for general purpose compu...
Data clustering methods have many applications in the area of data mining. Traditional clustering algorithms deal with quantitative or categorical data points. However, there exist...
Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzew...
We present a compiler optimization approach that uses the simulated evolution (SE) paradigm to enhance the finish time of heuristically scheduled computations with communication t...
This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic m...