Users of social networking sites often want to manage the sharing of information and content with different groups of people based on their differing relationships. However, group...
Data warehouses provide a great deal of opportunities for performing data mining tasks such as classification and clustering. Typically, updates are collected and applied to the d...
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
We present and analyze the star clustering algorithm. We discuss an implementation of this algorithm that supports browsing and document retrieval through information organization...
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
Fuzzy c-varieties (FCV) is one of the clustering algorithms in which the prototypes are multi-dimensional linear varieties. The linear varieties are represented by some local prin...
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Abstract. With information proliferation on the Web, how to obtain highquality information from the Web has been one of hot research topics in many fields like Database, IR as well...
Abstract. Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general pr...