summary.ppclust

Summarizes the clustering results for an object which is an instance of &#8216;ppclust&#8217; class.

cluster

Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.

Zeynel Cebeci

ppclust

Probabilistic and Possibilistic Cluster Analysis

Figen Yildiz

Alper Tuna Kavlak

Cagatay Cebeci

Hasan Onder

summary.ppclust function

<dl> <dt>object</dt>
<dd>an object of <code>ppclust</code> class to be summarized.</dd> <dt>...</dt>
<dd>additional arguments for S3 method summary.</dd></dl>

Arguments

Author

Summarize the clustering results — summary.ppclust

<dl>

 <dt>object</dt>
<dd>an object of <code>ppclust</code> class to be summarized.</dd>

 <dt>...</dt>
<dd>additional arguments for S3 method summary.</dd>

</dl>

summary.ppclust: Summarize the clustering results

Description

Usage

Value

Arguments

Author

See Also

Examples