In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discrimina...
Accurate demand forecasting remains difficult and challenging in today's competitive and dynamic business environment, but even a little improvement in demand prediction may ...
Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, informati...
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with ...
Background: Mining gene patterns that are common to multiple genomes is an important biological problem, which can lead us to novel biological insights. When family classification...
Meng Hu, Kwangmin Choi, Wei Su, Sun Kim, Jiong Yan...