clusteringgranular computingdata miningconcept hierarchyWe investigate a number of measures associated with partitions. The first of these is congruence measures, which are used to calculate the similarity betw
The hierarchical clustering methods may be applied to the data by using the cluster command or to a user-supplied dissimilarity matrix by using the clustermat com- mand. The cluster command has the following subcommands, which are detailed in their respective man- ual entries. Partition-...
This method performs vertical partitioning of the dataset by selecting the feature subset having maximum performance in a feature selection task. • Attribute clustering (AC) [145]: The clustering of features is carried out in this FSP approach. For the FSP, the most popular clustering methods ...
关键词: partitioning clustering granular computing data mining concept hierarchy DOI: 10.1007/978-3-642-05177-7_15 被引量: 1 年份: 2009 收藏 引用 批量引用 报错 分享 全部来源 求助全文 Springer Semantic Scholar 掌桥科研 dx.doi.org ResearchGate 查看更多 相似文献 参考文献 引证文献...
A 'Consensus Partition' in Computer Science refers to a final clustering result obtained by reconciling multiple candidate partitions generated by different clustering validation criteria or algorithms. AI generated definition based on: Temporal Data Mining Via Unsupervised Ensemble Learning, 2017 ...
Sunil Nadella, Kiranmai M V S V, Dr Narsimha Gugulotu, A Hybrid K-Mean-Grasp For Partition Based Clustering Of Two- Dimensional Data Space As An Application of P-Median Problem, International Journal of Computer and Electronics Research [Volume 1, Issue 1, June 2012] ISSN : 2278-5795...
clustering method according to the computer resources in computer cluster and through the vector of resource demand and the vector of lowest inaccuracy tolerance,we can divide the computer cluster into several classes(logical computer cluster) and make the every computer performance in one class to ...
Let \({\mathbf {c}}\) be any clustering labels obtained in the clustering step. Let \(\hat{{\mathcal {G}}}_{1}\) denote the estimated structure after estimating edges within clusters at line 3, and \(\tilde{{\mathcal {G}}}\) denote the final estimated skeleton at line 18. ...
Most of these partitional clustering approaches perform hard clustering, i.e. a data point can belong to only one cluster. In fuzzy clustering approaches, a data point can be assigned to more than one cluster with different membership values. Approaches based on fuzzy clustering have also been ...
Table 1 summarises the networks and shows the best modularity values across 10 runs of the clustering method, resetting the seed for the random number generator each time. In terms of modularity, our method performs best against all methods for all networks tested. Furthermore, for the karate,...