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# Change the Number and Size of Clusters

There are two main stages to the clustering process:

- Build Seeds

- Group Seeds into Clusters

## Build Seeds

IDOL Server builds seeds when you send the `ClusterSnapshot` action. IDOL Server takes a sample of the documents that it stores, and tries to associate individual documents with each other, based on the similarity of the concepts that the documents contain. Each group produced at this stage, containing a sample document and similar documents, is a seed.

IDOL Server stops trying to build a seed when the seed meets the requirements that `SeedSize` specifies or when there are no more documents that meet the similarity requirement that `SeedBindLevel` specifies (whichever condition is reached first). IDOL Server discards any seeds that do not reach the required size.

The number of clusters that you specify with `NumClusters` affects the number of sample documents that IDOL Server tries to create seeds from. You can adjust the relationship between the number you specify here and the size of the sample used by changing the value of `StartingSuggestOverrideFactor`.

## Group Seeds into Clusters

IDOL Server groups seeds into clusters when you send the `ClusterSGDataGen` or `ClusterCluster` actions. IDOL Server tries to create clusters by grouping seeds together. The grouping is based on the similarity of the concepts that the seeds or clusters contain.

Clustering is complete when one of the following conditions is met:

- IDOL Server creates the number of clusters specified by `NumClusters`.

- IDOL Server cannot create any more clusters that meet the similarity requirement specified by `BindLevel`.

IDOL Server discards clusters that do not meet the quality requirement set by `BindLevel` or the size requirement set by `MinClusterDocs`.

For details of the clustering actions, and the settings you can make to generate the clusters from your data, refer to the IDOL Server Reference.

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## Configuration Parameters

The ideal values for the parameters that affect clustering depend on the nature and amount of data in your IDOL Server. You can use the `SentientClustering` parameter for the `ClusterSnapshot` action to automatically determine the correct values for `SeedSize` and `SeedBindLevel`.

This section makes general recommendations about how to manually alter these parameters according to your data. Parameters are closely interdependent, so make these changes in combination with each other (rather than just changing one of the settings). Change values in small steps.

Although you can make many changes to clustering, the number and size of clusters that IDOL Server can identify depends ultimately on the data content that it contains. You can:

- cluster a small amount of data.
- cluster a large amount of data.
- cluster very similar data.
- cluster very different data.
- change the data view.

Cluster a Small Amount of Data

If your IDOL Server has a small amount of data, it is likely to identify fewer clusters, because it is less likely that your data contains a lot of similar documents for several different topics. You can edit the following parameters to change clustering in this situation.

Note: Ideally, your IDOL Server must contain at least 500 documents.

`SeedSize` | Decrease `SeedSize` (by three to four points at a time). This option reduces the size that seeds must reach, which means that more seeds are likely to be successfully created.
`MinClusterDocs` | Decrease `MinClusterDocs` so that clusters that contain fewer documents are not discarded.
`StartingSuggestOverrideFactor` | Increase `StartingSuggestOverrideFactor` (by one or two points only). This increases the number of sample documents from which IDOL Server creates seeds, which in some cases increases the possibility of finding clusters in the data.
`SeedBindLevel` | Decrease `SeedBindLevel` (by one point at a time) to reduce the similarity threshold for clusters. Do not change this value until you try changing `SeedSize`, because lowering `SeedBindLevel` is more likely to allow less-relevant documents into clusters.

Cluster a Large Amount of Data

If your IDOL Server has a large amount of data, you probably do not need to edit any clustering parameters, because this is the situation in which clustering is most successful. In some cases (for example, if your IDOL Server contains more than a million documents), it can be beneficial to alter the following parameter.

`StartingSuggestOverrideFactor` | Increase the value of this parameter to increase the number of sample documents from which IDOL Server creates seeds. This is sometimes necessary to allow a broader section of the data content to be represented by the clusters that are created.

Cluster Very Similar Data

If the documents in your IDOL Server contain highly similar concepts, IDOL Server might identify a small number of large clusters. For example, if your IDOL Server contains mostly documents about sports, then you might get one large sports cluster. This situation is a realistic characterization of the data in your IDOL Server, but in many circumstances is not useful. You can edit the following parameters to generate smaller, more specific clusters (for example, breaking sports into football, tennis, golf, and so on).

`SeedBindLevel` | Increase `SeedBindLevel` to require greater similarity between the documents that form a seed, which can reduce the breadth of topics covered by the concepts in the seed documents. Note: Increase `SeedBindLevel` one point at a time. Increasing by too much can result in seeds being discarded because they do not contain enough documents.
`BindLevel` | Increase `BindLevel` to require greater similarity between the concepts in seeds or clusters that merge to create a cluster. This change can decrease the size of clusters, as well as increase the number of clusters identified, because merging seeds and clusters together stops at an earlier stage.

Cluster Very Different Data

If the documents in your IDOL Server contain a wide variety of concepts, there might not be enough similar documents for IDOL Server to create seeds or clusters that characterize the data that it stores. You can lower the similarity requirement with the following parameters.

`SeedBindLevel` | Decrease `SeedBindLevel` to reduce the similarity requirement between the documents that form a seed, which can increase the breadth of topics covered by the concepts in the seed documents. Note: Decrease `SeedBindLevel` one point at a time. Decreasing by too much can result in seeds and clusters that contain documents that are less relevant, because the similarity requirement is too low.
`BindLevel` | Decrease `BindLevel` to reduce the similarity requirement between the concepts in seeds or clusters that merge to create a cluster. This change can increase the size of clusters, as well as increase the number of clusters identified (because fewer are discarded for not meeting the quality requirement).

Change the Data View

It might be the case that although IDOL Server identifies clusters that characterize your data successfully, you want to change the view of the data that clustering creates. The following parameters enable you to change the data view that clusters generate.

`NumClusters` | Increase `NumClusters` to obtain a more low-level view of your data by identifying more clusters. Decrease `NumClusters` to obtain a more high-level view by identifying fewer clusters.
`MinClusterDocs` | Decrease `MinClusterDocs` to reduce the number of clusters that are discarded. This option allows you to identify smaller clusters. Increase `MinClusterDocs` to increase the number of clusters that it discards. Only larger clusters are kept.
`BindLevel` | Decrease `BindLevel` to reduce the similarity requirement between the concepts in seeds or clusters that merge to create a cluster. This option can increase the size of clusters, as well as increase the number of clusters identified (because it discards fewer clusters for not meeting the quality requirement). Increase `BindLevel` to increase the similarity requirement between the concepts in seeds or clusters that merge to create a cluster. This can decrease the size of clusters, as well as increase the number of clusters identified, because merging seeds and clusters together stops at an earlier stage.

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