Abstract. The primary goal of cluster analysis is to produce clusters that accurately reflect the natural groupings in the data. A second objective that is important for high-dimensional data is to identify features that are descriptive of the clusters. In addition to these requirements, we often wish to allow objects to be associated with more than one cluster. In this paper we present a technique, based on the spectral co-clustering model, that is effective in meeting these objectives. Our evaluation on a range of text clustering problems shows that the proposed method yields accuracy superior to that afforded by existing techniques, while producing cluster descriptions that are amenable to human interpretation. 1

Abstract. Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy. 1