With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining highdimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the...

The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Such applications have to deal with high dimensional data sets with hundreds of dimensions. However, in high dimensional space, the data are sparse and the notion of proximity fails to retain its meaningfulness. Many recent algorithms use heuristics suc...