that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowl-edge bases for that purpose. Using this clustering, we can show that our

that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peer a given query should be forwarded. In this talk, I propose the use of graph clustering techniques on knowledge bases for that purpose. After a brief round-trip over an ontology-based

are mostly statistical (studying statistics such as degree distributions, hop-plots and clustering coefficients). These statistical methods are very useful, but the resolutions of the summaries are hard to control. In this paper, we introduce two database-style operations to summarize graphs. Like the OLAP

the ontologies amenable for automatic composition based on the accumulated knowledge rules, we represent them using a graph-oriented model extended with a small algebraic operator set. ONION, a user-friendly toolkit, aids the experts in bridging the semantic gap in real-life settings. Our framework provides a

present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization. We evaluate these techniques both in terms

Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors

of long video sequences based on behavioral content, and (iii) clustering events within long video sequence into event-consistent sub-sequences (i.e., into event-consistent “clusters”). These tasks are performed without prior knowledge of the types of events, their models, or their temporal extents. Our

Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we

This paper presents a method to model the images and their content descriptors in large image databases using bipartite graphs. A graph partitioning algorithm is then developed to cluster the images and their content description features simultaneously such that each cluster is automatically

. In this paper, we investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method, that finds the “knee ” in a ‘ # of clusters vs. clustering evaluation metric ’ graph. Using the knee