Weka_clusterers(RWeka)
Weka_clusterers()所属R语言包：RWeka
R/Weka Clusterers
/ Weka中Clusterers的
译者：生物统计家园网 机器人LoveR
描述----------Description----------
R interfaces to Weka clustering algorithms.
R接口Weka中的聚类算法。
用法----------Usage----------
Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
XMeans(x, control = NULL)
DBScan(x, control = NULL)
参数----------Arguments----------
参数：x
an R object with the data to be clustered.
一个R对象的数据以进行聚类。
参数：control
an object of class Weka_control, or a character vector of control options, or NULL (default). Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.
对象的类Weka_control，或一个字符矢量控制选项，或NULL（默认）。可用的选项可以使用在WEKA选项向导WOW，或在WEKA文档上线。
Details
详细信息----------Details----------
There is a predict method for predicting class ids or memberships from the fitted clusterers.
有一个predict方法预测的类ID或会员的拟合clusterers。
Cobweb implements the Cobweb (Fisher, 1987) and Classit (Gennari et al., 1989) clustering algorithms.
Cobweb实现：蛛网（费舍尔，1987），Classit（Gennari等人，1989）的聚类算法。
FarthestFirst provides the “farthest first traversal algorithm” by Hochbaum and Shmoys, which works as a fast simple approximate clusterer modeled after simple k-means.
FarthestFirst提供的“最远的优先遍历算法”由Hochbaum和Shmoys，这可以作为一个快速，简单，经过简单的k-近似聚类器为蓝本。
SimpleKMeans provides clustering with the k-means algorithm.
SimpleKMeans提供聚类与k-means算法。
XMeans provides k-means extended by an “Improve-Structure part” and automatically determines the number of clusters.
XMeans提供k延长“改善结构的一部分”，并自动确定簇的数量。
DBScan provides the “density-based clustering algorithm” by Ester, Kriegel, Sander, and Xu. Note that noise points are assigned to NA.
DBScan提供了“基于密度的聚类算法”由酯，Kriegel，砂光机，和许。需要注意的是噪声点被分配到NA。
值----------Value----------
A list inheriting from class Weka_clusterers with components including
继承类Weka_clusterers组件，包括列表
参数：clusterer
a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClusterer method to the training instances using the given control options.
参考（类jobjRef）将在WEKA buildClusterer方法的培训的情况下使用给定的控制选项到一个Java对象。
参数：class_ids
a vector of integers indicating the class to which each training instance is allocated (the results of calling the Weka clusterInstance method for the built clusterer and each instance).
一个向量整数，表示类，每个训练实例被分配（在WEKA clusterInstance内置的聚类器，每个实例的方法调用的结果）。
注意----------Note----------
XMeans requires Weka package XMeans to be installed.
XMeans需要Weka中包XMeans被安装。
DBScan requires Weka package optics_dbScan to be installed.
DBScan需要Weka中包optics_dbScan被安装。
参考文献----------References----------
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.
Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2/2, 139–172.
Models of incremental concept formation. Artificial Intelligence, 40, 11–62.
A best possible heuristic for the <code>k</code>-center problem, Mathematics of Operations Research, 10(2), 180–184.
X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727–734. Morgan Kaufmann.
Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
实例----------Examples----------
cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3))
cl1
table(predict(cl1), iris$Species)
## Not run: [＃不运行：]
## Requires Weka package 'XMeans' to be installed.[＃需要安装Weka中包“XMeans，。]
## Use XMeans with a KDTree.[与一的kdtree＃使用XMeans的。]
cl2 <- XMeans(iris[, -5],
c("-L", 3, "-H", 7, "-use-kdtree",
"-K", "weka.core.neighboursearch.KDTree -P"))
cl2
table(predict(cl2), iris$Species)
## End(Not run)[＃（不执行）]
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注：
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发表于 2012-9-29 20:56:31
