updated the docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402284

docs/docs/algorithms.xml

@@ -112,6 +112,7 @@
                <item>probabilistic_decision_function</item>
                <item>krls</item>
                <item>kcentroid</item>
+               <item>kkmeans</item>
                <item>svm_nu_train</item> 
                <item>svm_nu_train_prob</item> 
                <item>svm_nu_cross_validate</item> 
@@ -790,6 +791,23 @@
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+      
+      <component>
+         <name>kkmeans</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
+         <description>
+                This is an implementation of a kernelized k-means clustering algorithm.  
+                It performs k-means clustering by using the <a href="#kcentroid">kcentroid</a> object.  
+         </description>
+
+         <examples>
+            <example>kkmeans_ex.cpp.html</example>
+         </examples>
+                                 
+      </component>
+      
       
    <!-- ************************************************************************* -->
       

docs/docs/index.xml

@@ -119,6 +119,7 @@
                   <li><a href="algorithms.html#svm_nu_train">nu support vector machines</a> for classification</li>
                   <li>An online <a href="algorithms.html#krls">kernel RLS regression</a> algorithm</li>
                   <li>An online kernelized <a href="algorithms.html#kcentroid">centroid estimator</a></li>
+                  <li>A kernelized <a href="algorithms.html#kkmeans">k-means</a> clustering algorithm</li>
                   <li>Bayesian Network inference algorithms such as the 
                      <a href="algorithms.html#bayesian_network_join_tree">join tree</a> algorithm and 
                      <a href="algorithms.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> Markov Chain Monte Carlo algorithm</li>

docs/docs/main_menu.xml

@@ -91,6 +91,10 @@
                   <name>Kernel_Centroid</name>
                   <link>kcentroid_ex.cpp.html</link>
                </item>
+               <item>
+                  <name>Kernel_K-Means_Clustering</name>
+                  <link>kkmeans_ex.cpp.html</link>
+               </item>
                <item>
                   <name>Matrix</name>
                   <link>matrix_ex.cpp.html</link>

docs/docs/release_notes.xml

@@ -14,6 +14,7 @@
 New Stuff:
    - Added some macros that allow dlib to create a stack trace
    - Added a kernel based centroid estimator/novelty detector
+   - Added a kernel based k-means clustering algorithm
    - Added an identity_matrix() function that can take a runtime defined size.
    - Added a bunch of unconstrained optimization stuff to the library. 
      It now has a conjugate gradient optimization algorithm as well as

docs/docs/term_index.xml

@@ -391,6 +391,7 @@
          <term link="algorithms.html#point" name="point"/>
          <term link="algorithms.html#krls" name="krls"/>
          <term link="algorithms.html#kcentroid" name="kcentroid"/>
+         <term link="algorithms.html#kkmeans" name="kkmeans"/>
          
          <term link="dlib/svm/svm_abstract.h.html#maximum_nu" name="maximum_nu"/>