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Mean Shift Clustering via LPCM

Source: R/mean_shift_LPCM.R
details_mean_shift_LPCM.Rd

mean_shift() creates a mean shift clustering model.

Details

For this engine, there is a single mode: partition

Tuning Parameters

This model has 1 tuning parameters:

  • bandwidth: Bandwidth (type: double, default: no default)

Translation from tidyclust to the original package (partition)

mean_shift(bandwidth = 0.5) |>
  set_engine("LPCM") |>
  set_mode("partition") |>
  translate_tidyclust()

## Mean Shift Clustering Specification (partition)
##
## Main Arguments:
##   bandwidth = 0.5
##
## Computational engine: LPCM
##
## Model fit template:
## tidyclust::.mean_shift_fit_LPCM(x = missing_arg(), bandwidth = missing_arg(),
##     bandwidth = 0.5)

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), tidyclust will convert factor columns to indicators.

LPCM::ms() scales each variable internally to the unit range before applying the Gaussian kernel, so the bandwidth value lives on the scaled scale rather than the raw data scale. Bandwidths between roughly 0.05 and 1 are typical; smaller values find more clusters and larger values merge them.

What does it mean to predict?

To predict the cluster assignment for a new observation, the mean shift procedure is run from the new point until it converges to a mode. The observation is then assigned to the cluster of the nearest discovered training mode by Euclidean distance.

References

  • Cheng, Y. (1995). Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8), 790–799. https://ieeexplore.ieee.org/document/400568

  • Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. https://ieeexplore.ieee.org/document/1000236

  • Einbeck, J., Evers, L., & Hinchliff, K. (2010). Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.), Advances in Data Analysis, Data Handling and Business Intelligence (pp. 701–712). Springer.