Cluster labeling jobs are run against your data collections, and are used:
  • When clusters or well-defined document categories already exist
  • When you want to discover and attach keywords to see representative words within existing clusters
To create a cluster labeling job, sign in to Managed Fusion and click Collections > Jobs. Then click Add+ and in the Clustering and Outlier Analysis Jobs section, select Cluster Labeling. You can enter basic and advanced parameters to configure the job. If the field has a default value, it is populated when you click to add the job.

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Basic parameters

To enter advanced parameters in the UI, click Advanced. Those parameters are described in the advanced parameters section.
  • Spark job ID. The unique ID for the Spark job that references this job in the API. This is the id field in the configuration file. Required field.
  • Input/Output Parameters. This section includes these parameters:
    • Training collection. The Solr collection that contains documents associated with defined categories or clusters. The job will be run against this information. This is the trainingCollection field in the configuration file. Required field.
    • Output collection. The Solr collection where the job output is stored. The job will write the output to this collection. This is the outputCollection field in the configuration file. Required field.
    • Data format. The format that contains training data. The format must be compatible with Spark and options include solr, parquet, and orc. Required field.
  • Field Parameters. This section includes these parameters:
    • Field to detect keywords from. The field that contains the data that the job will use to discover keywords for the cluster. This is the fieldToVectorize field in the configuration file. Required field.
    • Existing document category field. The field that contains existing cluster IDs or document categories. This is the clusterIdField field in the configuration file. Required field.
    • Top frequent terms field name. The field where the job output stores top frequent terms in each cluster. Terms may overlap with other clusters. This is the freqTermField field in the configuration file. Optional field.
    • Top unique terms field name. The field where the job output stores the top frequent terms that, for the most part, are unique in each cluster. This is the clusterLabelField field in the configuration file. Optional field.
  • Model Tuning Parameters. This section includes these parameters:
    • Max doc support. The maximum number of documents that can contain the term. Values that are <1.0 indicate a percentage, 1.0 is 100 percent, and >1.0 indicates the exact number. This is the maxDF field in the configuration file. Optional field.
    • Min doc support. The minimum of documents that must contain the term. Values that are <1.0 indicate a percentage, 1.0 is 100 percent, and >1.0 indicates the exact number. This is the minDF field in the configuration file. Optional field.
    • Number of keywords for each cluster. The number of keywords required to label each cluster. This is the numKeywordsPerLabel field in the configuration file. Optional field.
  • Featurization Parameters. This section includes this parameter:
    • Lucene analyzer schema. This is the JSON-encoded Lucene text analyzer schema used for tokenization. This is the analyzerConfig field in the configuration file. Optional field.

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Advanced parameters

If you click the Advanced toggle, the following optional fields are displayed in the UI.
  • Spark Settings. The Spark configuration settings include:
    • Spark SQL filter query. This field contains the Spark SQL query that filters your input data. For example, SELECT * from spark_input registers the input data as spark_input. This is the sparkSQL field in the configuration file.
    • Data output format. The format for the job output. The format must be compatible with Spark and options include solr and parquet. This is the dataOutputFormat field in the configuration file.
    • Partition fields. If the job output is written to non-Solr sources, this field contains a comma-delimited list of column names that partition the dataframe before the external output is written. This is the partitionCols field in the configuration file.
  • Read Options. This section lets you enter parameter name:parameter value options to use when reading input from Solr or other sources. This is the readOptions field in the configuration file.
  • Write Options. This section lets you enter parameter name:parameter value options to use when writing output to Solr or other sources. This is the writeOptions field in the configuration file.
  • Dataframe config options. This section includes these parameters:
    • Property name:property value. Each entry defines an additional Spark dataframe loading configuration option. This is the trainingDataFrameConfigOptions field in the configuration file.
    • Training data sampling fraction. This is the fractional amount of the training data the job will use. This is the trainingDataSamplingFraction field in the configuration file.
    • Random seed. This value is used in any deterministic pseudorandom number generation to group documents into clusters based on similarities in their content. This is the randomSeed field in the configuration file.
  • Field Parameters. The advanced option adds this parameter:
    • Fields to load. This field contains a comma-delimited list of Solr fields to load. If blank, the job selects the required fields to load at runtime. This is the sourceFields field in the configuration file.
  • Miscellaneous Parameters. This section includes this parameter:
    • Model ID. The unique identifier for the model to be trained. If no value is entered, the Spark Job ID is used. This is the modelId field in the configuration file.
To create new clusters, use the Document Clustering job.

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Configuration properties