kneedle: Knee/Elbow Point Detection

In deadwood: Outlier Detection via Trimming of Mutual Reachability Minimum Spanning Trees

Knee/Elbow Point Detection

Description

Finds the most significant knee/elbow using the Kneedle algorithm with exponential smoothing.

Usage

kneedle_increasing(x, convex = TRUE, dt = 0.01)

Arguments

x	data vector (increasing)
convex	whether the data in x are convex-ish (elbow detection) or not (knee lookup)
dt	controls the smoothing parameter \alpha = 1-\exp(-dt) of the exponential moving average, y_i = \alpha x_i + (1-\alpha) x_{i-1}, y_1 = x_1

Value

Returns the index of the knee/elbow point; 1 if not found.

Author(s)

Marek Gagolewski

References

V. Satopaa, J. Albrecht, D. Irwin, B. Raghavan, Finding a "Kneedle" in a haystack: Detecting knee points in system behavior, In: 31st Intl. Conf. Distributed Computing Systems Workshops, 2011, 166-171, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/ICDCSW.2011.20")}

See Also

The official online manual of deadwood at https://deadwood.gagolewski.com/

deadwood documentation built on Feb. 20, 2026, 5:10 p.m.