TwoStep Cluster Analysis Options
Outlier Treatment. This group allows you to treat outliers specially during clustering if the cluster features (CF) tree fills. The CF tree is full if it cannot accept any more cases in a leaf node and no leaf node can be split.
- If you select noise handling and the CF tree fills, it will be regrown after placing cases in sparse leaves into a "noise" leaf. A leaf is considered sparse if it contains fewer than the specified percentage of cases of the maximum leaf size. After the tree is regrown, the outliers will be placed in the CF tree if possible. If not, the outliers are discarded.
- If you do not select noise handling and the CF tree fills, it will be regrown using a larger distance change threshold. After final clustering, values that cannot be assigned to a cluster are labeled outliers. The outlier cluster is given an identification number of –1 and is not included in the count of the number of clusters.
Memory Allocation. This group allows you to specify the maximum amount of memory in megabytes (MB) that the cluster algorithm should use. If the procedure exceeds this maximum, it will use the disk to store information that will not fit in memory. Specify a number greater than or equal to 4.
- Consult your system administrator for the largest value that you can specify on your system.
- The algorithm may fail to find the correct or specified number of clusters if this value is too low.
Variable standardization. The clustering algorithm works with standardized continuous variables. Any continuous variables that are not standardized should be left as variables in the To be Standardized list. To save some time and computational effort, you can select any continuous variables that you have already standardized as variables in the Assumed Standardized list.
Advanced Options
CF Tree Tuning Criteria. The following clustering algorithm settings apply specifically to the cluster features (CF) tree and should be changed with care:
- Initial Distance Change Threshold. This is the initial threshold used to grow the CF tree. If inserting a given case into a leaf of the CF tree would yield tightness less than the threshold, the leaf is not split. If the tightness exceeds the threshold, the leaf is split.
- Maximum Branches (per leaf node). The maximum number of child nodes that a leaf node can have.
- Maximum Tree Depth. The maximum number of levels that the CF tree can have.
- Maximum Number of Nodes Possible. This indicates the maximum number of CF tree nodes that could potentially be generated by the procedure, based on the function (b d+1 – 1) / (b – 1), where b is the maximum branches and d is the maximum tree depth. Be aware that an overly large CF tree can be a drain on system resources and can adversely affect the performance of the procedure. At a minimum, each node requires 16 bytes.
Cluster Model Update. This group allows you to import and update a cluster model generated in a prior analysis. The input file contains the CF tree in XML format. The model will then be updated with the data in the active file. You must select the variable names in the main dialog box in the same order in which they were specified in the prior analysis. The XML file remains unaltered, unless you specifically write the new model information to the same filename. See the topic TwoStep Cluster Analysis Output for more information.
If a cluster model update is specified, the options pertaining to generation of the CF tree that were specified for the original model are used. More specifically, the distance measure, noise handling, memory allocation, or CF tree tuning criteria settings for the saved model are used, and any settings for these options in the dialog boxes are ignored.
Note: When performing a cluster model update, the procedure assumes that none of the selected cases in the active dataset were used to create the original cluster model. The procedure also assumes that the cases used in the model update come from the same population as the cases used to create the original model; that is, the means and variances of continuous variables and levels of categorical variables are assumed to be the same across both sets of cases. If your "new" and "old" sets of cases come from heterogeneous populations, you should run the TwoStep Cluster Analysis procedure on the combined sets of cases for the best results.
To Set Options for TwoStep Cluster Analysis
This feature requires the Statistics Base option.
- From the menus choose:
- In the TwoStep Cluster Analysis dialog box, click Options.
- Change the settings as needed.