This paper aims to introduce the robustness against noise into the spectral clustering algorithm. First, we propose a warping model to map the data into a new space on the basis o...
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Incremental hierarchical text document clustering algorithms are important in organizing documents generated from streaming on-line sources, such as, Newswire and Blogs. However, ...
In this paper, we propose GAD (General Activity Detection) for fast clustering on large scale data. Within this framework we design a set of algorithms for different scenarios: (...
Jiawei Han, Liangliang Cao, Sangkyum Kim, Xin Jin,...
Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semisupervised clustering algo...