R/Weka Clusterers
R interfaces to Weka clustering algorithms.
Cobweb(x, control = NULL) FarthestFirst(x, control = NULL) SimpleKMeans(x, control = NULL) XMeans(x, control = NULL) DBScan(x, control = NULL)
x |
an R object with the data to be clustered. |
control |
an object of class |
There is a predict method for
predicting class ids or memberships from the fitted clusterers.
Cobweb implements the Cobweb (Fisher, 1987) and Classit
(Gennari et al., 1989) clustering algorithms.
FarthestFirst provides the “farthest first traversal
algorithm” by Hochbaum and Shmoys, which works as a fast simple
approximate clusterer modeled after simple k-means.
SimpleKMeans provides clustering with the k-means
algorithm.
XMeans provides k-means extended by an
“Improve-Structure part” and automatically determines the
number of clusters.
DBScan provides the “density-based clustering algorithm”
by Ester, Kriegel, Sander, and Xu. Note that noise points are assigned
to NA.
A list inheriting from class Weka_clusterers with components
including
clusterer |
a reference (of class
|
class_ids |
a vector of integers indicating the class to which
each training instance is allocated (the results of calling the Weka
|
XMeans requires Weka package XMeans to be installed.
DBScan requires Weka package optics_dbScan to be
installed.
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96), Portland, OR, 226–231. AAAI Press.
D. H. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2/2, 139–172. doi: 10.1023/A:1022852608280.
J. Gennari, P. Langley, and D. H. Fisher (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11–62.
D. S. Hochbaum and D. B. Shmoys (1985). A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2), 180–184. doi: 10.1287/moor.10.2.180.
D. Pelleg and A. W. Moore (2006). X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727–734. Morgan Kaufmann.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3))
cl1
table(predict(cl1), iris$Species)
## Not run:
## Requires Weka package 'XMeans' to be installed.
## Use XMeans with a KDTree.
cl2 <- XMeans(iris[, -5],
c("-L", 3, "-H", 7, "-use-kdtree",
"-K", "weka.core.neighboursearch.KDTree -P"))
cl2
table(predict(cl2), iris$Species)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.