We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users wit...
Jinzhu Gao, Jian Huang, C. Ryan Johnson, Scott Atc...
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
In many real-world applications, data cannot be accurately represented by vectors. In those situations, one possible solution is to rely on dissimilarity measures that enable a se...
We investigate the application of genetic algorithms (GAs) for recognizing real two-dimensional (2-D) or three-dimensional (3-D) objects from 2-D intensity images, assuming that th...
George Bebis, Evangelos A. Yfantis, Sushil J. Loui...
Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional ...