نتایج جستجو برای: greedy clustering method
تعداد نتایج: 1716229 فیلتر نتایج به سال:
Finite mixture modelling provides a framework for cluster analysis based on parsimonious Gaussian mixture models. Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering information. This enables the selection of a more parsimonious model, yielding more efficient estimates, a clearer interpretation and, often, imp...
In this work we apply the multilevel paradigm to optimizing the modularity of a graph clustering on parallel shared memory architectures. We improve upon the state of the art by introducing new methods for effectively and efficiently coarsening graphs with power-law degree distributions, detecting an unknown number of communities, and for performing greedy modularity refinement in parallel. Fin...
The biclustering, co-clustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or sub-matrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering pr...
We describe our participation in the Multilingual Summarization Evaluation MSE 2006 where multiple documents in English, Arabic and Arabic-English machine translations are used to create a brief 100 word summary in English. Our system output was evaluated using the automated ROUGE evaluation system. The greedy optimization technique used to ensure that summaries always obey the length constrain...
This paper presents HITS’ coreference resolution system that participated in the CoNLL2012 shared task on multilingual unrestricted coreference resolution. Our system employs a simple multigraph representation of the relation between mentions in a document, where the nodes correspond to mentions and the edges correspond to relations between the mentions. Entities are obtained via greedy cluster...
Existing geographic routing algorithms face serious challenges due to location errors, nonplanarity issues and overhead of location service. To solve these issues, we propose Topological Cluster Based Geographic Routing that combines topology-based routing and geographic routing. It is a localized routing scheme where the geographic routing is performed on an overlay network of topological clus...
Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. Ensemb...
wireless sensor network includes a large number of nodes which are distributed in a geographical location. the essential fact about wsn is that energy of nodes is limited. therefore, presenting proper solutions as an optimized routing is crucial to equally use energy of all nodes. in this paper we propose a method which performs routing in wsns using greedy approach. it is able to choose optimu...
Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bay...
Current bio-technologies allow sequencing of genomes from multiple organisms, that co-exist as communities within ecological environments. This collective genomic process (called metagenomics) has spurred the development of several computational tools for the quantification of abundance, diversity and role of different species within different communities. Unsupervised clustering algorithms (al...
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