fanny_membership()

fanny_membership() computes a fuzzy membership matrix on a dissimilarity matrix using FANNY (Fuzzy Analysis Clustering). In the Helske et al. (2024) workflow, this matrix feeds soft classification or pseudoclass regression.

For k >= 2, the underlying fanny() implementation is a Python port of R cluster::fanny (fanny.c), including deterministic default initialization and the caddy column-reordering step. For k = 1, Sequenzo returns the deterministic one-cluster shortcut documented below.

Function Usage

fanny_membership(
    diss,
    k,
    *,
    m=1.4,
    max_iter=500,
    tol=1e-15,
    ini_mem_p=None,
)

R / Literature Parameter Mapping

Sequenzo	R / packages	Notes
fanny_membership()	cluster::fanny(diss, k, diss=TRUE, memb.exp=m)	Python port of R cluster::fanny for tested k >= 2 cases; k = 1 is a deterministic Sequenzo shortcut
m	memb.exp in fanny	Helske et al. (2024) use 1.4
max_iter, tol	maxit, tol in fanny	Default tol=1e-15, same as R
ini_mem_p	iniMem.p in cluster::fanny	Rows must sum to 1

Entry Parameters

Parameter	Required	Type	Description
diss	✓	ndarray	Square (n, n) distance matrix.
k	✓	int	Number of clusters. For k >= 2, Sequenzo follows the R cluster::fanny convention: k <= n // 2 - 1. This is an R implementation constraint, not a general fuzzy-clustering bound. For k = 1, Sequenzo returns the deterministic one-cluster membership matrix directly.
m	✗	float	Fuzziness exponent (memb_exp in fanny). Must be > 1. Default 1.4 (Helske et al. 2024).
max_iter	✗	int	Maximum FANNY iterations. Default 500.
tol	✗	float	Relative convergence tolerance on the objective. Default 1e-15 (R default).
ini_mem_p	✗	ndarray / None	Optional initial (n, k) membership matrix with nonnegative entries and rows summing to 1. If None, R-style deterministic initialization is used.

There is no random_state parameter: FANNY initialization is deterministic unless you supply ini_mem_p, matching R cluster::fanny.

Returns

A tuple (U, highest_membership_indices):

Element	Type	Description
U	ndarray, shape (n, k)	Row-stochastic membership matrix. Each row sums to 1.
highest_membership_indices	ndarray, shape (k,)	Row index with highest membership in each cluster column after R-style column reordering. These are not PAM medoids.

Pass U to soft_classification_variables() or pseudoclass_regression().

Do not pass highest_membership_indices to representativeness_matrix(). Use PAM medoid indices from medoid_indices_from_kmedoids_result() instead.

The same index vector can be obtained from a membership matrix with highest_membership_indices_from_membership().

Example

from sequenzo import fanny_membership, soft_classification_variables

U, hi_idx = fanny_membership(diss, k=5, m=1.4)
X_soft = soft_classification_variables(U, reference=0, as_dataframe=True, ids=seqdata.ids)

print(U.shape, U.sum(axis=1)[:3])

R Counterpart

• Closest R function: cluster::fanny(diss, k, diss=TRUE, memb.exp=m)
• Mapping note: For k >= 2, Sequenzo's underlying fanny() follows R cluster fanny.c, including the caddy column-reordering step, R's multi-cluster k constraint, default tol, and convergence reporting. For k = 1, Sequenzo uses the deterministic one-cluster shortcut because membership is necessarily all ones.

Lower-Level FANNY API

Advanced users can call fanny() directly from sequenzo.clustering.sequences_to_variables (or sequenzo.clustering). It returns a FannyResult dataclass:

Field	Description
membership	(n, k) membership matrix
clustering	(n,) crisp cluster ids after reordering
memb_exp	Fuzziness exponent used
objective	Final objective value
converged	Whether the algorithm converged within max_iter and tol
iterations	Iteration count when converged; -1 if not converged (R convention)
k_crisp	Number of crisp clusters after reordering
partition_coefficient, normalized_coefficient	Fuzzy partition quality measures

If the algorithm does not converge, fanny() emits a warning (as R does) and sets iterations = -1.

medoid_membership_approximation() is a fast heuristic: it runs PAM once, sets u_ik ∝ (1/d_ik)^(1/(m−1)), normalizes rows, then forces each medoid row to hard membership in its own cluster column. It is not exact FANNY and is not the Helske soft-classification default.

highest_membership_indices_from_membership()

highest_membership_indices_from_membership(membership)

Returns the row index with highest membership in each column of a membership matrix. These indices are not PAM medoids. fanny_membership() calls this helper on the FANNY result before returning.

Notes

• Default FANNY initialization follows R when ini_mem_p is None; there is no random seed to set.
• For k == 1, fanny() returns the deterministic one-cluster membership matrix. In practice Helske-style workflows use k >= 2, and R-parity tests should be interpreted for the multi-cluster case.
• Use PAM medoids for representativeness; use FANNY membership for soft classification.

See Also

• Section overview maps the surrounding workflow and related functions.
• Typical Workflow shows where this method fits in the full analysis.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

References

Helske, S., Helske, J., & Chihaya, G. K. (2024). From sequences to variables: Rethinking the relationship between sequences and outcomes. Sociological Methodology, 54(1), 27–51.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.