Apache Solr Reference Guide Covering Apache Solr 6.0

clustering When true, clustering component is enabled.
clustering.engine
Declares which clustering engine to use. If not present, the first declared engine
will become the default one.
clustering.results When true, the component will perform clustering of search results (this
should be enabled).
clustering.collection When true, the component will perform clustering of the whole document
index (this section does not cover full-index clustering).
At the engine declaration level, the following parameters are supported.
Parameter
Description
carrot.algorithm
carrot.resourcesDir
The algorithm class.
Algorithm-specific resources and configuration files (stop words, other
lexical resources, default settings). By default points to conf/clustering
/carrot2/
carrot.outputSubClusters If true and the algorithm supports hierarchical clustering, sub-clusters will
also be emitted.
carrot.numDescriptions
Maximum number of per-cluster labels to return (if the algorithm assigns
more than one label to a cluster).
The carrot.algorithm parameter should contain a fully qualified class name of an algorithm supported by
the Carrot2 framework. Currently, the following algorithms are available:
org.carrot2.clustering.lingo.LingoClusteringAlgorithm (open source)
org.carrot2.clustering.stc.STCClusteringAlgorithm (open source)
org.carrot2.clustering.kmeans.BisectingKMeansClusteringAlgorithm (open source)
com.carrotsearch.lingo3g.Lingo3GClusteringAlgorithm (commercial)
For a comparison of characteristics of these algorithms see the following links:
http://doc.carrot2.org/#section.advanced-topics.fine-tuning.choosing-algorithm
http://project.carrot2.org/algorithms.html
http://carrotsearch.com/lingo3g-comparison.html
The question of which algorithm to choose depends on the amount of traffic (STC is faster than Lingo, but
arguably produces less intuitive clusters, Lingo3G is the fastest algorithm but is not free or open source),
expected result (Lingo3G provides hierarchical clusters, Lingo and STC provide flat clusters), and the input data
(each algorithm will cluster the input slightly differently). There is no one answer which algorithm is "the best".
Contextual and Full Field Clustering
The clustering engine can apply clustering to the full content of (stored) fields or it can run an internal highlighter
pass to extract context-snippets before clustering. Highlighting is recommended when the logical snippet field
contains a lot of content (this would affect clustering performance). Highlighting can also increase the quality of
clustering because the content passed to the algorithm will be more focused around the query (it will be
query-specific context). The following parameters control the internal highlighter.
Parameter
Description
Apache Solr Reference Guide 6.0
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