PUMA publications for /tag/clustering%20theoryWed Jan 18 15:45:34 CET 2012Berlin / HeidelbergGraph-Theoretic Concepts in Computer Science121-132Lecture Notes in Computer ScienceOn Finding Graph Clusterings with Maximum Modularity47692007clustering graph modularity theory Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts, and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomaration approach.Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and Applications530-544LNAIContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so 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 provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.Tue May 04 08:55:46 CEST 2010SIAM J. MATRIX ANAL. APPLIC.3430--452{Partitioning Sparse Matrices with Eigenvectors of Graphs}111990clustering community graph partitioning spectral theory Tue May 04 08:55:46 CEST 2010{Some uses of spectral methods}2000clustering graph spectral svd theory Tue May 04 08:55:46 CEST 2010Berkeley, CA, USASpectral Partitioning Works: Planar Graphs and Finite Element Meshes1996clustering community detection graph spectral survey theory Tue May 04 08:55:46 CEST 2010IBM Journal of Research and Development5420--425{Lower bounds for the partitioning of graphs}171973clustering community detection graph spectral theory Tue May 04 08:55:46 CEST 2010Advances in Neural Information Processing Systems 14849--856On spectral clustering: Analysis and an algorithm2001clustering community detection graph spectral theory Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1Tue May 04 08:55:46 CEST 2010Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining({KDD}-2003)89--98Information-Theoretic Co-Clustering2003clustering co-clustering dhillon information theory toread Tue Jan 15 10:33:14 CET 2008Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJune530-544LNCSContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph myown ontology theory Wed Sep 20 18:23:30 CEST 2006HeidelbergThe Semantic Web: Research and Applications530-544LNAIContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so 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 provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.Tue Jan 24 08:42:05 CET 2006MarchThe structure and function of complex networks2003algorithm clustering complex_systems folksonomy information kdubiq network retrieval scale_free_networks small socialnetwork summerschool theory web web_graph world Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.clustering theoryCommunity for tag(s) clustering theory