标题: ant cluster tool box [打印本页] 作者: szfjnu 时间: 2010-12-4 21:58 标题: ant cluster tool box This is a Matlab toolbox for investigating the application of cluster ensembles to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster ensemble problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate ensemble data partitioning, and (4) implementation of accuracy metrics. . v" v# O% i5 N) W" vWith regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.5 W" z7 h6 ]( A' {8 M$ r/ m
作者: szfjnu 时间: 2010-12-4 21:58
This is a Matlab toolbox for investigating the application of cluster ensembles to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster ensemble problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate ensemble data partitioning, and (4) implementation of accuracy metrics. 1 n( v' @9 L1 D2 [ 3 t- Y: B% H- F1 K$ tWith regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.; ?9 E) v/ U1 @2 D 作者: liweineng0304 时间: 2010-12-4 22:22
这是什么东西啊？作者: 李子 时间: 2010-12-4 22:28
好吧 有是英文的东西！！！！！作者: wangsongtao 时间: 2010-12-5 03:35
是不是英文的作者: 坏女孩 时间: 2011-10-9 22:08
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