An implementation of the Component-wise Peak-Finding (CPF) clustering method, presented in 'A Theoretical Analysis of Density Peaks Clustering and the CPF Algorithm'.

CPFcluster supports Python 3, with numpy, scipy, itertools, multiprocessing and scikit-learn. These should be linked with a BLAS implementation (e.g., OpenBLAS, ATLAS, Intel MKL).

CPFcluster is available on PyPI, the Python Package Index.

$ pip install CPFcluster

To use CPFcluster, first import the CPFcluster module.

    from CPFcluster import CPFcluster

A CPFcluster object is constructed using the fit method, which returns a clustering of a dataset.

    CPF = CPFcluster(k, rho, alpha, n_jobs, remove_duplicates, cutoff)
    CPF.fit(X)

CPFcluster takes 6 arguments:

• k Number of nearest-neighbors used to create connected components from the dataset and compute the density.
• rho (Defaults to 0.4) Parameter used in threshold for center selection.
• alpha (Defaults to 1) Optional parameter used in threshold of edge weights for center selection, not discussed in paper.
• n_jobs (Defaults to 1) Number of cores for program to execute on.
• remove_duplicates (Defaults to False) Option to remove duplicate rows from data in advance of clustering.
• cutoff (Defaults to 1) Threshold for removing instances as outliers. Instances with fewer edges than the cutoff value are removed.

The CPFcluster object is then fit to a dataset:

• X An n-by-d numpy.ndarray with training data. The rows correspond to n observations, and the columns correspond to d dimensions.

The result object further contains:

• CCmat An n-by-n sparse matrix representation of the k-NN graph.
• components A vector containing the index of the component to which each instance belongs. If the instance is an outlying point, the value will be NaN.
• labels_ The final cluster labelings.

To replicate the experiments in the original paper:

python3 run_CPF.py

CPFcluster has an MIT License.

See LICENSE.