Analyzing data to find trends, correlations, and stable patterns is an important problem for many industrial applications. In this paper, we propose a new technique based on paral...
Kaidi Zhao, Bing Liu, Thomas M. Tirpak, Andreas Sc...
We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document abou...
Jeonghee Yi, Tetsuya Nasukawa, Razvan C. Bunescu, ...
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial pro...
Machine learning techniques for data extraction from semistructured sources exhibit different precision and recall characteristics. However to date the formal relationship between...
Guizhen Yang, Saikat Mukherjee, I. V. Ramakrishnan
We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Tradit...
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...
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...