predict

predict.ewkm

object of class <code>ewkm</code>.

object

the data that needs to be predicted. Variables should
 have the same names and order as used in building the
 <code><a rd-options="" href="/link/ewkm?package=wskm&version=1.4.40" data-mini-rdoc="wskm::ewkm">ewkm</a></code> model.

data

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Return the nearest cluster to each observation based on a
 Euclidean distance with each variable weighted differently per cluster.

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

predict.ewkm function

the data that needs to be predicted. Variables should
 have the same names and order as used in building the
 <code><a rd-options='' href='ewkm'>ewkm</a></code> model.

predict.ewkm: Predict method for `ewkm` model.

Description

Usage

Arguments

Value

See Also