graph clustering
Analysis of Graph Clustering Method
... data clustering has vital importance in various domains such as social network analysis, epidemiology, World Wide Web analysis, ...The clustering technique derives underlying structures present in the ...in ...
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Local expansion and optimization for higher order graph clustering
... in graph clustering is known as higher-order graph ...traditional graph clustering, higher-order graph clustering is used to identify clusters with tighter connections ...
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A graph clustering algorithm based on a clustering coefficient for weighted graphs
... the clustering ten- dency of the nodes of a graph is the clustering coefficient measure ...The clustering coefficient of a node measures how much its neighbors are close to a clique, ...find ...
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Axioms for Graph Clustering Quality Functions
... by graph clustering quality functions, that is, functions that assign a score to a clustering of a ...graph. Graph clustering, also known as network community detection, is often ...
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Pedestrian Re identification by Graph Clustering
... The graph clustering results on Dataset3 are showed in Table 3. We can see SC, CAC perform better than SymNMF and all the three methods perform relatively poor. In fact, the difference of the images number ...
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Computing Lexical Chains with Graph Clustering
... One of the principles of building lexical chains is that each term must belong to exactly one chain. If several chains are possible, Morris and Hirst (1991) choose the chain to whose overall score the term contributes ...
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Communication-Optimal Distributed Dynamic Graph Clustering
... in graph clustering for two distributed communication models to reduce communica- tion ...distributed graph clustering, and their method does not need to compute approximate effec- tive ...
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CORECLUSTER: A Degeneracy Based Graph Clustering Framework
... Graph clustering or community detection constitutes an im- portant task for investigating the internal structure of graphs, with a plethora of applications in several ...for graph clustering, ...
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A Practical Comparison of Local Graph Clustering Algorithms
... Graph clustering traditional algorithms need the whole graph for processing that is very expensive computations so that this paper focuses on local graph clustering ...Local ...
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Template-Based Graph Clustering
... Spectral Graph Clustering. Spectral Graph Clustering [20] is a popular technique for clustering data organized as ...a graph, or similarity graphs can be built from the ...a ...
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Distributed graph clustering and sparsification
... clusters. Graph clustering is an important research topic in many disciplines, including computer science, biology, and ...instance, graph clustering is widely used in finding communities in ...
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Graph Clustering: Algorithms, Analysis and Query Design
... of clustering n items into K disjoint clusters using noisy answers obtained from crowdsourced workers to pairwise queries of the type: “Are items i and j from the same clus- ...crowdsourced clustering which ...
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Unsupervised Learning of A-Morphous Inflection with Graph Clustering
... This paper presents a new approach to unsupervised learning of inflection. The problem is defined as two clusterings of the input wordlist: into lexemes and into forms. Word-Based Morphology is used to describe ...
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Graph Based Clustering for Computational Linguistics: A Survey
... on graph-based clustering, either done by theoreticians or ...ious clustering algorithms by taking advantage of elegant mathematical structures built in graph ...the graph ...
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A New Parametric Estimation Method for Graph based Clustering
... the graph, thus spectral clustering can be interpreted as trying to find a partition of the graph such that the random walk stays long within the same cluster and seldom jumps between clusters (von ...
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Efficient clustering of big data using graph method
... use. Clustering is a main task of exploratory data s the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other ...
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Hierarchical Verb Clustering Using Graph Factorization
... class. The majority represent a high number of classes and fewer members per class. Yet many of the clusters make syntactic and semantic sense. A good example is a cluster which includes member verbs from 9.7 Spray/Load ...
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A Fuzzy Graph-based Heuristic Algorithm of Possibilistic Clustering
... The novel FG-AFC-algorithm of possibilistic clustering is proposed in the paper. The algorithm is based on the idea of detecting of components of the initial fuzzy graph and constructing fuzzy clusters of ...
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Graph based text representation for document clustering
... Document clustering is considered a vital technology in the era of ...Text clustering means finding the groups that are related to each ...fact, clustering becomes very famous for its ability to ...
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A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction
... Regularity Clustering Framework to Hypergraphs: As described earlier, one of the most attractive notions of pairwise clustering methods is that they give a more ”global” view of the ...”global” ...
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