In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of...
Xiangji Huang, Yan Rui Huang, Miao Wen, Aijun An, ...
We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via the...
Sequence data are abundant in application areas such as computational biology, environmental sciences, and telecommunications. Many real-life sequences have a strong segmental str...
We describe a large-scale application of methods for finding plagiarism and self-plagiarism in research document collections. The methods are applied to a collection of 284,834 d...
Daria Sorokina, Johannes Gehrke, Simeon Warner, Pa...
Among the visual features of multimedia content, shape is of particular interest because humans can often recognize objects solely on the basis of shape. Over the past three decad...
High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...
The ultimate goal of data mining is to extract knowledge from massive data. Knowledge is ideally represented as human-comprehensible patterns from which end-users can gain intuiti...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Various extensions of NMF have also been proposed. In this paper we present an over...
It has been well recognized that data mining is an interactive and iterative process. In order to support this process, one of the long-term goals of data mining research has been...
Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. The power of data mining tools to extract hidden information that can...