how can ı find to distances of items in the k-means ?

Selim

hello everybody first off,how are you ? ı have been working on a warehouse lay out projects that ı did clustering with k-means algorthm but ı need to place my items to warehouse so ı need to  find distances rıght now so how can ı find to distances of the items each other ? 
----------------------- xml-------------------------

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David_A

Ok,

found an easier solution by using the already existing "Cross Distance" operator (thx @mschmitz for the hint).

<?xml version="1.0" encoding="UTF-8"?><process version="9.3.000-BETA2"><br>  <context><br>    <input/><br>    <output/><br>    <macros/><br>  </context><br>  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process" origin="GENERATED_TUTORIAL"><br>    <parameter key="logverbosity" value="init"/><br>    <parameter key="random_seed" value="2001"/><br>    <parameter key="send_mail" value="never"/><br>    <parameter key="notification_email" value=""/><br>    <parameter key="process_duration_for_mail" value="30"/><br>    <parameter key="encoding" value="SYSTEM"/><br>    <process expanded="true"><br>      <operator activated="true" class="retrieve" compatibility="9.3.000-BETA2" expanded="true" height="68" name="Retrieve Iris" origin="GENERATED_TUTORIAL" width="90" x="45" y="187"><br>        <parameter key="repository_entry" value="//Samples/data/Iris"/><br>      </operator><br>      <operator activated="true" class="concurrency:k_means" compatibility="9.0.001" expanded="true" height="82" name="Clustering" origin="GENERATED_TUTORIAL" width="90" x="179" y="187"><br>        <parameter key="add_cluster_attribute" value="true"/><br>        <parameter key="add_as_label" value="false"/><br>        <parameter key="remove_unlabeled" value="false"/><br>        <parameter key="k" value="3"/><br>        <parameter key="max_runs" value="10"/><br>        <parameter key="determine_good_start_values" value="false"/><br>        <parameter key="measure_types" value="BregmanDivergences"/><br>        <parameter key="mixed_measure" value="MixedEuclideanDistance"/><br>        <parameter key="nominal_measure" value="NominalDistance"/><br>        <parameter key="numerical_measure" value="EuclideanDistance"/><br>        <parameter key="divergence" value="SquaredEuclideanDistance"/><br>        <parameter key="kernel_type" value="radial"/><br>        <parameter key="kernel_gamma" value="1.0"/><br>        <parameter key="kernel_sigma1" value="1.0"/><br>        <parameter key="kernel_sigma2" value="0.0"/><br>        <parameter key="kernel_sigma3" value="2.0"/><br>        <parameter key="kernel_degree" value="3.0"/><br>        <parameter key="kernel_shift" value="1.0"/><br>        <parameter key="kernel_a" value="1.0"/><br>        <parameter key="kernel_b" value="0.0"/><br>        <parameter key="max_optimization_steps" value="100"/><br>        <parameter key="use_local_random_seed" value="true"/><br>        <parameter key="local_random_seed" value="1992"/><br>      </operator><br>      <operator activated="true" class="extract_prototypes" compatibility="9.3.000-BETA2" expanded="true" height="82" name="Extract Cluster Prototypes" width="90" x="313" y="34"><br>        <description align="center" color="transparent" colored="false" width="126">Extracts the Cluster Center,&lt;br/&gt;in this case the centroid</description><br>      </operator><br>      <operator activated="true" class="cross_distances" compatibility="9.3.000-BETA2" expanded="true" height="103" name="Cross Distances" width="90" x="581" y="187"><br>        <parameter key="measure_types" value="MixedMeasures"/><br>        <parameter key="mixed_measure" value="MixedEuclideanDistance"/><br>        <parameter key="nominal_measure" value="NominalDistance"/><br>        <parameter key="numerical_measure" value="EuclideanDistance"/><br>        <parameter key="divergence" value="GeneralizedIDivergence"/><br>        <parameter key="kernel_type" value="radial"/><br>        <parameter key="kernel_gamma" value="1.0"/><br>        <parameter key="kernel_sigma1" value="1.0"/><br>        <parameter key="kernel_sigma2" value="0.0"/><br>        <parameter key="kernel_sigma3" value="2.0"/><br>        <parameter key="kernel_degree" value="3.0"/><br>        <parameter key="kernel_shift" value="1.0"/><br>        <parameter key="kernel_a" value="1.0"/><br>        <parameter key="kernel_b" value="0.0"/><br>        <parameter key="only_top_k" value="false"/><br>        <parameter key="k" value="10"/><br>        <parameter key="search_for" value="nearest"/><br>        <parameter key="compute_similarities" value="false"/><br>      </operator><br>      <connect from_op="Retrieve Iris" from_port="output" to_op="Clustering" to_port="example set"/><br>      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/><br>      <connect from_op="Clustering" from_port="clustered set" to_op="Cross Distances" to_port="reference set"/><br>      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Cross Distances" to_port="request set"/><br>      <connect from_op="Cross Distances" from_port="result set" to_port="result 1"/><br>      <portSpacing port="source_input 1" spacing="0"/><br>      <portSpacing port="sink_result 1" spacing="0"/><br>      <portSpacing port="sink_result 2" spacing="0"/><br>      <portSpacing port="sink_result 3" spacing="0"/><br>      <description align="center" color="yellow" colored="false" height="129" resized="true" width="218" x="533" y="298">The &amp;quot;request&amp;quot; column is the cluster ID and the &amp;quot;document&amp;quot; is the example.&lt;br/&gt;&lt;br/&gt;cluster_0 is then request 1</description><br>    </process><br>  </operator><br></process><br><br>

David_A

Hi @Selim ,

unfortunately I couldn't check your process without the Excel file and the Python script.

But if I understood question correctly, you want to know the distance of an item to it's cluster center.

To do so you can calculate the distance yourself, as I did in the sample process below.

Best,
David

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a1_center)^2+ (a2 - a2_center)^2+ (a3 - a3_center)^2+ (a4 - a4_center)^2)"/><br>        </list><br>        <parameter key="keep_all" value="true"/><br>        <description align="center" color="transparent" colored="false" width="126">Calcute the Euclidean between the examples and the corresponding centroid</description><br>      </operator><br>      <connect from_op="Retrieve Iris" from_port="output" to_op="Clustering" to_port="example set"/><br>      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/><br>      <connect from_op="Clustering" from_port="clustered set" to_op="Join" to_port="right"/><br>      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Rename by Replacing" to_port="example set input"/><br>      <connect from_op="Rename by Replacing" from_port="example set output" to_op="Join" to_port="left"/><br>      <connect from_op="Join" from_port="join" to_op="Generate Attributes" to_port="example set input"/><br>      <connect from_op="Generate Attributes" from_port="example set output" to_port="result 1"/><br>      <portSpacing port="source_input 1" spacing="0"/><br>      <portSpacing port="sink_result 1" spacing="0"/><br>      <portSpacing port="sink_result 2" spacing="0"/><br>    </process><br>  </operator><br></process><br><br>