twkm.sample

A sample dataset of 410 variables and 2000 observations. The last variable is the class variable, and should be removed before clustering.
 The grouping of the variables are "0-75;76-291;292-355;356-402;403-408".

datasets

Entropy weighted k-means (ewkm) by Liping Jing, Michael
K. Ng and Joshua Zhexue Huang (2007)
<doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering
algorithm that is well suited to very high dimensional data.
Weights are calculated as the importance of a variable with
regard to cluster membership. The two-level variable
weighting clustering algorithm tw-k-means (twkm) by Xiaojun
Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013)
<doi:10.1109/TKDE.2011.262> introduces two types of weights,
the weights on individual variables and the weights on
variable groups, and they are calculated during the clustering
process. The feature group weighted k-means (fgkm) by Xiaojun
Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012)
<doi:10.1016/j.patcog.2011.06.004> extends this concept by
grouping features and weighting the group in addition to
weighting individual features.

He Zhao

wskm

Weighted k-Means Clustering

Graham Williams

Joshua Z Huang

Xiaojun Chen

Qiang Wang

Longfei Xiao

twkm.sample function

The <code>twkm.sample</code> dataset is a data frame with 2000 observations
 and 50 variables.

Format

Sample dataset to test the twkm algorithm. — twkm.sample

Sample dataset to test the twkm algorithm.

twkm.sample: Sample dataset to test the twkm algorithm.

Description

Usage

Arguments

Format

See Also