SOCR EduMaterials AnalysisActivities HierarchicalClustering - Revision history

IvoDinov: /* Hierarchical Clustering Example: Primate Evolution */

2012-07-29T20:05:09Z

‎Hierarchical Clustering Example: Primate Evolution

IvoDinov at 20:03, 29 July 2012

2012-07-29T20:03:06Z

IvoDinov: added See also: SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture

2012-07-28T20:06:22Z

added See also: SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture

IvoDinov: /* Overview */

2012-07-28T20:02:50Z

‎Overview

IvoDinov at 18:07, 28 July 2012

2012-07-28T18:07:00Z

IvoDinov: /* Hierarchical Clustering Example: Primate Evolution */

2012-07-28T17:41:11Z

‎Hierarchical Clustering Example: Primate Evolution

IvoDinov: /* Hierarchical Clustering Example: Primate Evolution */

2012-07-28T17:40:07Z

‎Hierarchical Clustering Example: Primate Evolution

IvoDinov: /* Hierarchical Clustering Example: Primate Evolution */

2012-07-28T17:35:14Z

‎Hierarchical Clustering Example: Primate Evolution

IvoDinov at 17:24, 28 July 2012

2012-07-28T17:24:29Z

IvoDinov: /* Hierarchical Clustering Example: Primate Evolution */

2012-07-28T17:20:21Z

‎Hierarchical Clustering Example: Primate Evolution

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 <center>[[Image:SOCR_AnalysisActivities_HierarchicalClustering_Fig2.png|500px]]</center>
-* In the SOCR Hierarchical Clustering analysis, there is one SOCR built-in example. Click on the '''Example 1''' button. You should see the data displayed in 5 columns: '''Barcelona, Madrid, San Sebastian, Sevilla,''' and '''Valencia'''.
+* In the SOCR Hierarchical Clustering analysis, there is one SOCR built-in example. Click on the '''Example 1''' button.
-<center>[[Image:SOCR_AnalysisActivities_HierarchicalClustering_Fig3.png|500px]]</center>
+The dendrogram to the right of the menu bar summarizes the results of Hierarchical Clustering analysis (using unweighted average method) nicely. To change the method used to calculate the distances between clusters, click on the drop-down menu to the right of “Clustering Algorithm” inside the menu bar, and you will see a list of supported methods: Single linkage, complete linage, unweighted average, weighted average, unweighted centroid, weighted centroid and joint between-within. After selecting, click on the “Update” button on the top left, and the dendrogram will then be updated according to the method selected. The user can simultaneous load a text file of correctly formatted data without affecting the current open dendrogram.
 As you notice, the dendrogram is a preferable way of displaying the algorithm of hierarchical clustering, as it gives the user a birds-eye view of the merging process without losing significant information.

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 </center>
-===Hierarchical Clustering Example===
+===Hierarchical Clustering Example: Primate Evolution===
-We will demonstrate Hierarchical Clustering using a SOCR built-in example. The data is the distances (in kilometer) between five Spanish cities, and it is represented as a symmetric matrix with headings. The five objects (cities) are: Barcelona, Madrid, San Sebastian, Sevilla, and Valencia.
+We will demonstrate Hierarchical Clustering using a SOCR built-in example. DNA data representing the molecular phylogeny and evolution of primate mitochondrial DNA is obtained from [[SOCR_EduMaterials_AnalysisActivities_HierarchicalClustering#References|Hayasaka, Gojobori, and Horai (1988)]]. In this homoplasy example, the primate distance matrix was computed by [[SOCR_EduMaterials_AnalysisActivities_HierarchicalClustering#References|Makarenkov and Legendre, (2004)]] and is shown below:
 * As you start the [http://socr.ucla.edu/htmls/ana/ SOCR Analyses Applet], click on '''Hierarchical Clustering''' from the combo box in the left panel. Here's what the screen should look like.
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 As you notice, the dendrogram is a preferable way of displaying the algorithm of hierarchical clustering, as it gives the user a birds-eye view of the merging process without losing significant information.
 <center>

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 ===Note===
 If you happen to click on the '''Clear''' button in the middle of the procedure, all the data will be cleared out. Simply start over from step 1 and click on an EXAMPLE button for the data you want.
 ===References===

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 === Overview ===
-This SOCR Activity demonstrates the utilization of the [http://socr.ucla.edu/htmls/ana/ SOCR Analyses package] for statistical Computing. In particular, it shows how to use [http://socr.ucla.edu/htmls/ana/ Hierarchical Clustering] and how to interpret the output results.
+This SOCR Activity demonstrates the utilization of the [http://socr.ucla.edu/htmls/ana/ SOCR Analyses package] for statistical computing. In particular, it shows how to use [http://socr.ucla.edu/htmls/ana/ Hierarchical Clustering] and how to interpret the output results.
 ===Background===

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 This SOCR Activity demonstrates the utilization of the [http://socr.ucla.edu/htmls/ana/ SOCR Analyses package] for statistical Computing. In particular, it shows how to use [http://socr.ucla.edu/htmls/ana/ Hierarchical Clustering] and how to interpret the output results.
-==Background==
+===Background===
 [http://en.wikipedia.org/wiki/Hierarchical_clustering Hierarchical Clustering] is a technique of clustering analysis which aims to build a hierarchy (top-down ordering) of clusters. Hierarchical clustering is classified as a connectivity-based clustering algorithm, as it builds models based on distance connectivity (i.e., distances between different clusters). There are various ways to compute the distances – for example, in the single linkage method, the distance between two clusters is computed as the distance between the two closest elements in the two clusters, whereas in the complete linkage method, it is computed as the distance between the two farthest elements in the two clusters. In essence, the paradigm of hierarchical clustering boils down to the following steps:
 # Designate every object/item to a new cluster - if you have X items, then there should be X clusters, with each of them containing just one object.

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 * Note that this classification agrees with the phylogenetic tree hierarchy previously reported:
-<center>[[Image:SOCR_AnalysisActivities_HierarchicalClustering_Fig7.gif|500px]]</center>
+<center>[[Image:SOCR_AnalysisActivities_HierarchicalClustering_Fig7.gif|300px]]</center>
 ===Note===

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 </center>
-* You can also load these data in the [http://socr.ucla.edu/htmls/SOCR_3DCharts.html SOCR 3D Viewer] (requires Java 3D) to see the distance matrix as an image gradient in 3D.
+* You can also load these data in the [http://socr.ucla.edu/htmls/SOCR_3DCharts.html SOCR 3D Viewer] (requires Java 3D) (specify 12x12) to see the distance matrix as an image gradient in 3D.
 <center>[[Image:SOCR_AnalysisActivities_HierarchicalClustering_Fig8.png|500px]]</center>

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 |}
 </center>
 * Load the data into the [http://socr.ucla.edu/htmls/ana/ SOCR Hierarchic Clustering Applet], as shown below: