aldaoud: Initialization of cluster prototypes using Al-Daoud's...

In inaparc: Initialization Algorithms for Partitioning Cluster Analysis

Initialization of cluster prototypes using Al-Daoud's algorithm

Description

Initializes the cluster prototypes matrix using the variance-based algorithm proposed by Al-Daoud (Al-Daoud, 2005).

Usage

aldaoud(x, k)

Arguments

x	a numeric vector, data frame or matrix.
k	an integer specifying the number of clusters.

Details

At first, the algorithm finds the feature having the greatest variance and sorts the data set on this feature in any order. Then it divides the data set into n/k-length k segments. The medians of the segments are assigned as the protoypes of clusters. Al-Daoud's algorithm is likely to be effective only for data sets in which the variability is mostly on one dimension because it considers only one feature with the highest variance (Celebi et al, 2013).

Value

an object of class ‘inaparc’, which is a list consists of the following items:

v	a numeric matrix containing the initial cluster prototypes.
sfidx	an integer for the column index of the feature with the highest variance.
ctype	a string for the type of used centroid to determine the cluster prototypes. It is ‘med’ with this function.
call	a string containing the matched function call that generates this ‘inaparc’ object.

Author(s)

Zeynel Cebeci, Cagatay Cebeci

References

Al-Daoud, M.B. (2005). A new algorithm for cluster initialization, in Proc. of 2nd World Enformatika Conf., pp.74-76.

Celebi, M.E., Kingravi, H.A. & Vela, P.A. (2013). A comparative study of efficient initialization methods for the K-means clustering algorithm, Expert Systems with Applications, 40 (1): 200-210. arXiv:https://arxiv.org/pdf/1209.1960.pdf

See Also

ballhall, crsamp, firstk, forgy, hartiganwong, inofrep, inscsf, insdev, kkz, kmpp, ksegments, ksteps, lastk, lhsmaximin, lhsrandom, maximin, mscseek, rsamp, rsegment, scseek, scseek2, spaeth, ssamp, topbottom, uniquek, ursamp

Examples

data(iris)
res <- aldaoud(iris[,1:4], k=5)
v <- res$v
print(v)

inaparc documentation built on June 16, 2022, 5:09 p.m.