SpectralBiclustering

See method documentation: implementation and assumptions vary.

{
  "variable": "purchases",
  "domain": "Retail product affinity analysis",
  "rows": [
    "new_customers",
    "casual_customers",
    "members",
    "loyal_members"
  ],
  "columns": [
    "food",
    "beverages",
    "electronics",
    "homewares"
  ],
  "values": [
    [
      9,
      8,
      1,
      1
    ],
    [
      8,
      9,
      1,
      1
    ],
    [
      1,
      1,
      9,
      8
    ],
    [
      1,
      1,
      8,
      9
    ]
  ]
}

# DataLens.ai example
# Domain: Retail product affinity analysis.
# The sample data above are included automatically when copied.
import numpy as np
from sklearn.cluster import SpectralBiclustering

# Build the numeric feature matrix from named customer attributes.
# Fit or evaluate the requested clustering technique.
# Study a segment/product purchase matrix, not a geometric point cloud.
purchases_matrix = np.array(purchases["values"], dtype=float)
model = SpectralBiclustering(n_clusters=(2,2), random_state=0).fit(purchases_matrix)
assert len(model.row_labels_) == 4 and len(set(model.row_labels_)) == 2 and len(set(model.column_labels_)) == 2

# All checks passed; no exception was raised.
print("Expected output verified")

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dag_longest_path_length dijkstra_path dijkstra_path_length find_cycle floyd_warshall floyd_warshall_numpy goldberg_radzik johnson lexicographical_topological_sort maximum_spanning_edges maximum_spanning_tree minimum_cycle_basis minimum_spanning_edges minimum_spanning_tree multi_source_dijkstra negative_edge_cycle random_spanning_tree shortest_path shortest_path_length simple_cycles single_source_dijkstra topological_sort transitive_closure transitive_reduction adamic_adar_index attribute_assortativity_coefficient betweenness_centrality closeness_centrality cn_soundarajan_hopcroft communicability_betweenness_centrality current_flow_betweenness_centrality degree_assortativity_coefficient degree_centrality edge_betweenness_centrality eigenvector_centrality group_betweenness_centrality harmonic_centrality in_degree_centrality information_centrality jaccard_coefficient katz_centrality load_centrality numeric_assortativity_coefficient out_degree_centrality preferential_attachment ra_index_soundarajan_hopcroft resource_allocation_index second_order_centrality subgraph_centrality trophic_levels within_inter_cluster asyn_fluidc asyn_lpa_communities average_clustering clustering edge_betweenness_partition girvan_newman greedy_modularity_communities kernighan_lin_bisection label_propagation_communities louvain_communities modularity partition_quality square_clustering transitivity triangles all_node_cuts average_node_connectivity boykov_kolmogorov capacity_scaling dinitz edge_connectivity edge_disjoint_paths edmonds_karp k_edge_components k_edge_subgraphs local_edge_connectivity local_node_connectivity max_flow_min_cost maximum_flow min_cost_flow minimum_cut minimum_edge_cut minimum_node_cut network_simplex node_connectivity node_disjoint_paths preflow_push shortest_augmenting_path max_clique max_weight_matching maximal_matching maximum_independent_set min_maximal_matching min_weight_matching min_weighted_vertex_cover braycurtis canberra cdist chebyshev cityblock 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uniform_filter watershed_ift white_tophat zoom cKDTree ConvexHull Delaunay distance_matrix geometric_slerp HalfspaceIntersection KDTree procrustes SphericalVoronoi Voronoi cholesky eig eigh eigvals eigvalsh expm fractional_matrix_power hessenberg logm lu ordqz polar qr qz schur sqrtm svd Shannon entropy