We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and...
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is aut...
Xiaolei Li, Jiawei Han, Sangkyum Kim, Hector Gonza...
Data cubes as employed by On-Line Analytical Processing (OLAP) play a key role in many application domains. The analysis typically involves to compare categories of different hie...
Harald Piringer, Matthias Buchetics, Helwig Hauser...
Several marketing problems involve prediction of customer purchase behavior and forecasting future preferences. We consider predictive modeling of large scale, bi-modal or multimo...