Programmer Guide/Command Reference/EVAL/haclust - Revision history

193.171.195.48 at 09:46, 21 April 2011

2011-04-21T09:46:35Z

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	----		----
	;See also: For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].		;See also: For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].
	:[[../modclust\|modclust]], [[../em\|em]], [[../density\|density]], [[../svd\|svd]]		:[[../modclust\|modclust]], [[../em\|em]], [[../density\|density]], [[../svd\|svd]], [[../dist\|dist]]

	[[../#Functions\|<function list>]]		[[../#Functions\|<function list>]]

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	;Usage 2:<code>haclust(<var>htable</var>, <var>nclust</var>)</code> ... create partition table:		;Usage 2:<code>haclust(<var>htable</var>, <var>nclust</var>)</code> ... create partition table:
	;Usage 3:<code>haclust(<var>ptable</var>, <var>iclust</var>)</code> ... extract cluster data:		;Usage 3:<code>haclust(<var>ptable</var>, <var>iclust</var>)</code> ... extract cluster data:
	~~:For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].~~
	----		----
	;See also: [[../modclust\|modclust]], [[../em\|em]], [[../density\|density]], [[../svd\|svd]]		;See also: For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].
			:[[../modclust\|modclust]], [[../em\|em]], [[../density\|density]], [[../svd\|svd]]

			[[../#Functions\|<function list>]]


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	exit		exit
	</pre>		</pre>

	~~[[../#Functions\|<function list>]]~~

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	;Usage 1:<code>haclust(<var>d</var>, <var>m</var>)</code>		;Usage 1:<code>haclust(<var>d</var>, <var>m</var>)</code>
	:;<var>d</var>: A dissimilarity or distance matrix. For N data points this is a NxN matrix (e.g. created using the function [[../dist\|dist]])		:;<var>d</var>: A dissimilarity or distance matrix. For N data points this is a NxN matrix (e.g. created using the function [[../dist\|dist]])
	:;<var>m</var>:The cluster method:		:;<var>mflag</var>:The cluster method:
	::{\|class="einrahmen"		::{\|class="einrahmen"
	!''m'' !! method		!''mflag'' !! method
	\|-		\|-
	\|0 \|\|Single Linkage		\|0 \|\|Single Linkage
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	:For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].		:For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].
	----		----
	;See also: [[../modclust\|modclust]]		;See also: [[../modclust\|modclust]], [[../em\|em]], [[../density\|density]], [[../svd\|svd]]

2011-04-21T05:37:31Z

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	\|6 \|\|Ward		\|6 \|\|Ward
	\|}		\|}
	;Result 1: The result is a cluster hierarchy. This is a 3x(N-1) table containing the merged cluster pairs and the agglomeration niveaus.		;Result 1: The result is a cluster hierarchy table ''htable''. This is a 3x(N-1) table containing the merged cluster pairs and the agglomeration niveaus.
	----		----
	;Usage 2:<code>haclust(<var>h</var>, <var>c</var>)</code>		;Usage 2:<code>haclust(<var>htable</var>, <var>nclust</var>)</code> ... create partition table:
	:;<var>h</var>~~:A cluster hierarchy (e.g. computed with Usage 1).~~		;Usage 3:<code>haclust(<var>ptable</var>, <var>iclust</var>)</code> ... extract cluster data:
	:;<var>c</var>:~~The number~~ of ~~clusters.~~		:For a detailed description of '''Usage 2''' and '''Usage 3''' and the format of the hierarchy ''htable'' and the partition table ''ptable'' see [[../modclust\|modclust]].
	~~;Result~~ 2~~:The created~~ partition~~, which is a 1xN~~ table ~~containing the group indices~~.
	----		----
	;See also: [[../modclust\|modclust]]		;See also: [[../modclust\|modclust]]

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	:;<var>h</var>:A cluster hierarchy (e.g. computed with Usage 1).		:;<var>h</var>:A cluster hierarchy (e.g. computed with Usage 1).
	:;<var>c</var>:The number of clusters.		:;<var>c</var>:The number of clusters.
	;Result:The created partition, which is a 1xN table containing the group indices.		;Result 2:The created partition, which is a 1xN table containing the group indices.
	----		----
	See also: [[../modclust\|modclust]]		;See also: [[../modclust\|modclust]]

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	Example:		Example:
	<pre>		<pre>
	~~// A simple demonstration of <code>haclust</code> usage.~~
	[macro haclust_example]		[macro haclust_example]
	// create bivariate input data		// create bivariate input data

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	;Usage 1:<code>haclust(<var>d</var>, <var>m</var>)</code>		;Usage 1:<code>haclust(<var>d</var>, <var>m</var>)</code>
	:;<var>d</var>: A dissimilarity or distance matrix. For N data points this is a NxN matrix (e.g. created using the function [[../dist\|dist]])		:;<var>d</var>: A dissimilarity or distance matrix. For N data points this is a NxN matrix (e.g. created using the function [[../dist\|dist]])
	:;<var>m</var>The cluster method:		:;<var>m</var>:The cluster method:
	::{\|class="einrahmen"		::{\|class="einrahmen"
	!''m'' !! method		!''m'' !! method

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@@ Line 1: / Line 1: @@
 {{DISPLAYTITLE:{{SUBPAGENAME}}}}
 Hierarchical-agglomerative cluster analysis.
 <pre>
 [macro haclust_example]
-        // create bivariate input data
+// create bivariate input data
-        #datatable := new table * 2 /p num:x num:y
+#datatable := new table * 2 /p num:x num:y
-        $#datatable[*,0] := '1;2;1;3;4.5'
+$#datatable[*,0] := '1;2;1;3;4.5'
-        $#datatable[*,1] := '1;0.5;4;2;4'
+$#datatable[*,1] := '1;0.5;4;2;4'
-        // compute distance matrix
+// compute distance matrix
-        #disttable := eval dist($#datatable)
+#disttable := eval dist($#datatable)
-        // cluster analysis methods:
+// cluster analysis methods:
-        // 0 (SL), 1 (CL), 2 (WAL), 3 (AL), 4 (M), 5 (C), 6 (W)
+// 0 (SL), 1 (CL), 2 (WAL), 3 (AL), 4 (M), 5 (C), 6 (W)
-        #method := 6    // Ward
+#method := 6    // Ward
-        // nr of clusters
+// nr of clusters
-        #nclusters := 2
+#nclusters := 2
-        // compute hierarchy and partition
+// compute hierarchy and partition
-        #hierarchy := eval haclust($#disttable,$#method)
+#hierarchy := eval haclust($#disttable,$#method)
-        #partition := eval haclust($#hierarchy,$#nclusters)
+#partition := eval haclust($#hierarchy,$#nclusters)
-        // show results
+// show results
-        showitem $#hierarchy
+showitem $#hierarchy
-        showitem $#partition
+showitem $#partition
 exit
 </pre>