A synthetic data set described by Pal et al (2005). It consists of two continous variables forming two well-separated clusters in addition to two noise.

datasets

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

x12 function

A data frame with 12 rows and 2 numeric variables:<dl>
 <dt>p1</dt>
<dd>a numeric variable ranging from -5.0 to 5.0</dd> <dt>p2</dt>
<dd>a numeric variable ranging from -1.67 to 10.0</dd> 
</dl>

Format

Synthetic data set of two variables — x12

A data frame with 12 rows and 2 numeric variables:<dl>
 <dt>p1</dt>
<dd>a numeric variable ranging from -5.0 to 5.0</dd>

 <dt>p2</dt>
<dd>a numeric variable ranging from -1.67 to 10.0</dd>

 
</dl>

x12: Synthetic data set of two variables

Description

Usage

Arguments

Format

References

Examples