Terms by Cluster Summary

>>> from sklearn.cluster import KMeans
>>> kmeans = KMeans(
...     n_clusters=4,
...     init="k-means++",
...     n_init=10,
...     max_iter=300,
...     tol=0.0001,
...     algorithm="lloyd",
...     random_state=0,
... )
>>> from techminer2.packages.document_clustering import TermsByClusterSummary
>>> (
...     TermsByClusterSummary()
...     #
...     # FIELD:
...     .with_field("raw_keywords")
...     .having_terms_in_top(50)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # COUNTERS:
...     .using_term_counters(True)
...     #
...     # TFIDF:
...     .using_binary_term_frequencies(False)
...     .using_row_normalization(None)
...     .using_idf_reweighting(False)
...     .using_idf_weights_smoothing(False)
...     .using_sublinear_tf_scaling(False)
...     #
...     # CLUSTERING:
...     .using_clustering_algorithm_or_dict(kmeans)
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
   Cluster  ...                                              Terms
0        0  ...  SUSTAINABILITY 03:0227; SUSTAINABLE_DEVELOPMEN...
1        1  ...  FINTECH 32:5393; FINANCE 11:1950; INNOVATION 0...
2        2  ...  MARKETPLACE_LENDING 03:0317; LENDINGCLUB 02:02...
3        3  ...  CONTENT_ANALYSIS 02:0181; DIGITALIZATION 02:01...

[4 rows x 4 columns]