Recently, data mining over uncertain data streams has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications....
Clustering is an important technique for understanding and analysis of large multi-dimensional datasets in many scientific applications. Most of clustering research to date has be...
Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, calle...
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constra...
Data mining applications analyze large collections of set data and high dimensional categorical data. Search on these data types is not restricted to the classic problems of minin...