About Clustering Models

A clustering model is an unsupervised learning algorithm that groups similar records or similar variables. For example, if you want to identify an operation in a production process, or variables that have similar behaviour.

K-Means

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K-Means K-Means

For more information, see the online learning platform

K-means clustering is a method which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. 

To launch this model tool, select Models > K-Means from the menu. Alternatively, click the corresponding icon in the sidebar.

Tip
title Create variable set
It is possible to create a new variable set using the input of the model. Click the icon Image Added.

Create a K-Means

The parameters for a curve are defined on two tabs at the top of the page: Data and Properties.

On the Data tab: 

1. Select a Data source from the list.
2. Select an Object set from the list.
3. ... 

Control the View

Use the control menu below the chart to modify the zoom, set rulers, apply new object sets and export. See [x-ref] for more information.

 this method are defined as follows: 

1. Enter Model name. 
2. Enter Datasource from the list (if applicable). 
3. Select a Learning set from the list.
4. Enter a Cluster name prefix. The default prefix is "CLUSTER".
5. Enter a Cluster number, default 3. For more information, see Cluster number. 
6. Enter a Maximum number of iterations. For more information, see Maximum number of iterations. 
7. Select Cluster Standardisation: 
   
   1. None: no cluster standardization. 

   2. Normalize: 

   3. Normalize: transform the variable to have a max of 1 and a min of 0. The value is calculated with: scaled(x) = (x - min)/(max - min) where the min and max values are based on the learning data set.

   4. Standardize: transform the variable to have a mean of 0 and a standard deviation of 1. The value is calculated with: scaled(x) = (x - µ)/(STDEV) where the average (µ) and standard deviation (STDEV) values are calculated on the learning data set.

8. Select Calculate cluster silhouette checkbox. For more information, see Calculate cluster silhouette. 
9. Select Optimize cluster number checkbox. For more information, see Optimize cluster number . 
10. Select variable(s) from the list for the Inputs.
11. Click Save to generate the clusters. 

Subclu

Subclu is an unsupervised clustering algorithm used to define groups or patterns with the data based on the density of data points. It marks as outlier’s points that lie alone in low-density regions. Each cluster is expanded one dimension at a time into a dimension that is known to have a cluster that only differs from previous clusters in one dimension. Therefore, it is not necessary to define the number of clusters as in k-Means.

Create Subclu

The parameters for this method are defined as follows: 

1. Enter Model name. 
2. Select a Datasource from the list (if applicable).
3. Select a Learning set from the list.
4. Select variable(s) from the list for the Input.
5. Enter Cluster name prefix. The default prefix is ("CLUSTER-"). 
6. Enter Variable suffix. The default suffix is ("SUBCLU-1 Cluster").  
7. Enter Epsilon, default 0.1. For more information, see Epsilon. 
8. Select Calculate cluster silhouette: yes or no.  For more information, see Calculate cluster silhouette.
9. Enter a Minimum points, default 10. For more information, see Minimum points. 
10. Enter  Min cluster Dimensions.  For more information, see Min cluster Dimensions. 
11. Select Cluster Standardisation: 
    
    1. None: no cluster standardization. 

    2. Normalize: transform the variable to have a max of 1 and a min of 0. The value is calculated with: scaled(x) = (x - min)/(max - min) where the min and max values are based on the learning data set.

    3. Standardize: transform the variable to have a mean of 0 and a standard deviation of 1. The value is calculated with: scaled(x) = (x - µ)/(STDEV) where the average (µ) and standard deviation (STDEV) values are calculated on the learning data set.

12. Click Save to generate the clusters.  

IMS

Inductive Monitoring System (IMS) is a new clustering method available in DATAmaestro based on the distance between data points. It is not necessary to define the number of clusters as in k-Means. IMS looks point by point if they can be included in an existing cluster, firstly by comparing the min & max values in each dimension and secondly by comparing the distance compared to the center of the cluster. If the point cannot be included in an existing cluster, a new cluster will be created. 

Create IMS

The parameters for this method are defined as follows: 

1. Enter Model name. 
2. Select a Datasource from the list (if applicable).
3. Select a Learning set from the list.
4. Enter Cluster name prefix. The default prefix is ("CLUSTER-"). 
5. Select Standardisation: 
   
   1. None: no cluster standardization. 

   2. Normalize: transform the variable to have a max of 1 and a min of 0. The value is calculated with: scaled(x) = (x - min)/(max - min) where the min and max values are based on the learning data set.

   3. Standardize: transform the variable to have a mean of 0 and a standard deviation of 1. The value is calculated with: scaled(x) = (x - µ)/(STDEV) where the average (µ) and standard deviation (STDEV) values are calculated on the learning data set.

6. Enter Epsilon, default 0.1. Epsilon is the maximal distance for a point to be in a cluster. A larger value tends to lead to a lower number of clusters.
7. Select Calculate cluster silhouette: yes or no.  For more information, see Calculate cluster silhouette.
8. Enter Variable name prefix The default prefix is ("IMS-1 Cluster").  
9. Select the Variable Set.
10. Select variable(s) from the list for the Variable. 
11. Click Save to generate the clusters.  

Hierarchical Clustering

For more information, see the online learning platform

Hierarchical clustering is a model that is viewed as a dendrogram. A dendrogram is a tree diagram frequently used to illustrate the arrangement of the clusters produced by hierarchical clustering. For more information, see Dendrograms.