Abstract--Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining appro...
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu, Geoff...
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst...
The similarity join is an important database primitive which has been successfully applied to speed up applications such as similarity search, data analysis and data mining. The s...
We present a new visualization of the distance and cluster structure of high dimensional data. It is particularly well suited for analysis tasks of users unfamiliar with complex d...
We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of o...