The exponential rise of online content in the form of blogs, microblogs, forums, and multimedia sharing sites has raised an urgent demand for e±cient and high-quality text clustering algorithms for fast navigation and browsing of users based on better document organization. For several kinds of these user-generated content, it is much easier to obtain the input in small sets, where the data in each set belongs to the same class but with unknown class labels. Such data is viewed as weakly-labeled data and the inherent chunklet information is very useful for improving clustering performance. In this paper, we propose a system - CluChunk (clustering chunklet data) to cluster unlabeled web data which incorporates chunklet information. We try to transfer the original feature space by a discriminatively learning linear transformation such that simple unsupervised learning techniques (such as K-Means) in the transformed space can achieve good clustering accuracy. Using larger scale data from some web applications (social media and online forums), we demonstrate that the clustering performance can get significantly improved by: 1)incorporating the inherent weakly-labeled information into the clustering framework; 2)enriching the representation of short text with additional features extracted from the chunklet subset. The proposed approach can be applied to other mining tasks with large scale user-generated content, like product review summarizing and blog content clustering/classification task.