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我正在使用Rapid Miner查看k-Medoids算法的一些结果和性能。我能够创建该方案并查看输出结果,但是我希望在每个集群内部创建中心点(所选的medoid)有没有办法做到这一点?RapidMiner - k-Medoids。识别medoid
我正在使用Rapid Miner查看k-Medoids算法的一些结果和性能。我能够创建该方案并查看输出结果,但是我希望在每个集群内部创建中心点(所选的medoid)有没有办法做到这一点?RapidMiner - k-Medoids。识别medoid
可以使用Extract Cluster Prototypes
运算符来创建设置对应聚类中心的例子。然后,您可以使用Append
运算符将其加入到原始数据中,尽管为了使示例集兼容,还有一些工作要做。
下面是一个例子
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="7.0.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.0.001" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="7.0.001" expanded="true" height="68" name="Retrieve Iris" width="90" x="45" y="34">
<parameter key="repository_entry" value="//Samples/data/Iris"/>
</operator>
<operator activated="true" class="k_medoids" compatibility="7.0.001" expanded="true" height="82" name="Clustering" width="90" x="179" y="136">
<parameter key="k" value="3"/>
</operator>
<operator activated="true" class="generate_attributes" compatibility="7.0.001" expanded="true" height="82" name="Generate Attributes" width="90" x="313" y="136">
<list key="function_descriptions">
<parameter key="type" value=""data""/>
</list>
</operator>
<operator activated="true" class="select_attributes" compatibility="7.0.001" expanded="true" height="82" name="Select Attributes" width="90" x="447" y="136">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="label|id"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="extract_prototypes" compatibility="7.0.001" expanded="true" height="82" name="Extract Cluster Prototypes" width="90" x="313" y="34"/>
<operator activated="true" class="generate_attributes" compatibility="7.0.001" expanded="true" height="82" name="Generate Attributes (2)" width="90" x="447" y="34">
<list key="function_descriptions">
<parameter key="type" value=""centroid""/>
</list>
</operator>
<operator activated="true" breakpoints="before" class="append" compatibility="7.0.001" expanded="true" height="103" name="Append" width="90" x="581" y="85"/>
<connect from_op="Retrieve Iris" from_port="output" to_op="Clustering" to_port="example set"/>
<connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
<connect from_op="Clustering" from_port="clustered set" to_op="Generate Attributes" to_port="example set input"/>
<connect from_op="Generate Attributes" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Append" to_port="example set 2"/>
<connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Generate Attributes (2)" to_port="example set input"/>
<connect from_op="Generate Attributes (2)" from_port="example set output" to_op="Append" to_port="example set 1"/>
<connect from_op="Append" from_port="merged set" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
的type
属性可以在情节被用作颜色,所以你可以看到的重心。
如果我记得正确,rapidminer中的K-medoids是一个有缺陷的实现。我相信它并没有实际使用k-medoids,而是k-means的非标准变体。但是自从我看着rapidminer进行集群以来,这已经有一段时间了,因为我更喜欢ELKI。 –