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CLUSTERING

SpectralCoclustering

Spectral Co-Clustering algorithm (Dhillon, 2001) [1]_.

Example of usage

Python API reference included; executable data demonstration pending review.

Usage / limitations

+Use when: Discover latent groups.

−Limitations: Check input requirements, mathematical assumptions and version-specific behaviour before using.

Mathematical formula

See method documentation: implementation and assumptions vary.
Sample dataShow 
{
  "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
    ]
  ]
}

Code example (Python)

# 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 SpectralCoclustering

# Build the numeric feature matrix from named customer attributes.
# Fit or evaluate the requested clustering technique.
# Segment customer cohorts and product groups at the same time.
purchases_matrix = np.array(purchases["values"], dtype=float)
model = SpectralCoclustering(n_clusters=2, random_state=0).fit(purchases_matrix)
assert len(model.row_labels_) == 4 and len(set(model.row_labels_)) == 2

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