fastmath.clustering — generateme/fastmath 2.1.5

Clustering.

Various clustering algrorithms backed by SMILE library.

Only partition clustering is implemented.

Input data

It's always sequence of n-sized samples as sequences.

For example, 2d samples [[1 2] [2 2] [3 3] ...]

For 1d data you can pass sequence of numbers of sequence of 1d seqs of numbers

[1 2 3]
;; or
[[1] [2] [3]]

Distances

Some of the methods use distance functions, use fastmath.distance namespace to create one.

Output

Every function returns record which contains:

• :type - name of the method used
• :data - input data
• :clustering - sequence of cluster ids
• :sizes - sizes of clusters
• :clusters - number of clusters
• :predict - predicting function (see below), qualify additional sample
• :representatives - list of centroids or averages
• :info - additional statistics for your samples (like distortion)
• :obj - SMILE object

Cluster id is a integer ranging from 0 to the number of clusters minus 1. Some methods mark outliers with outlier-id.

Record acts as function and can qualify additonal sample by calling :predict function (or just call predict), for example (data is sequence of 3d samples):

(let [cl (k-means data 10)] (cl [0 1 2]))

See k-means

Regrouping

Clustering record can be regroupped to the list of individual clusters. Call regroup and get list of maps with following structure:

• :key - cluster id or :outliers
• :data - samples which belong to the cluster
• :representative - centroid or average vector if the former is not available
• :size - size of cluster

clarans clj

(clarans data clusters)

(clarans data dist clusters)

(clarans data dist clusters max-neighbor)

Clustering Large Applications based upon RANdomized Search algorithm.

Input:

• data - sequence of samples
• dist (optional) - distance method, default euclidean
• clusters - number of clusters
• max-neighbor (optional) - maximum number of neighbors checked during random search

See more in SMILE doc

clustering-methods-list clj

List of clustering methods.

ClusteringResult clj

dbscan clj

(dbscan data min-pts radius)

(dbscan data dist min-pts radius)

Density-Based Spatial Clustering of Applications with Noise algorithm.

Input:

• data - sequence of samples
• dist (optional) - distance method, default euclidean
• min-pts - minimum number of neighbors
• radius - the neighborhood radius

See more in SMILE doc

denclue clj

(denclue data sigma m)

(denclue data sigma m tolerance)

(denclue data sigma m tolerance min-pts)

DENsity CLUstering algorithm.

Input:

• data - sequence of samples
• sigma - gaussian kernel parameter
• m - number of selected samples, much smaller than number of all samples
• tolerance (optional) - tolerance of hill-climbing procedure
• min-pts (optional) - minimum number of neighbors for a core attractor

See more in SMILE doc

deterministic-annealing clj

(deterministic-annealing data max-clusters)

(deterministic-annealing data max-clusters alpha)

(deterministic-annealing data max-clusters alpha max-iter)

(deterministic-annealing data max-clusters alpha max-iter tolerance)

(deterministic-annealing data
                         max-clusters
                         alpha
                         max-iter
                         tolerance
                         split-tolerance)

Deterministic Annealing algorithm.

Input:

• data - sequence of samples
• max-clusters - number of clusters
• alpha (optional) - temperature decreasing factor (valued from 0 to 1)
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of convergence test
• split-tolerance (optional) - tolerance to split a cluster

See more in SMILE doc

g-means clj

(g-means data clusters)

(g-means data clusters max-iter)

(g-means data clusters max-iter tolerance)

G-Means

Input:

• data - sequence of samples
• clusters - number of clusters
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of convergence test

See more in SMILE doc

k-means clj

(k-means data clusters)

(k-means data clusters max-iter)

(k-means data clusters max-iter tolerance)

K-Means++ algorithm.

Input:

• data - sequence of samples
• clusters - number of clusters
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of convergence test

See more in SMILE doc

lloyd clj

(lloyd data clusters)

(lloyd data clusters max-iter)

(lloyd data clusters max-iter tolerance)

K-Means algorithm, lloyd.

Input:

• data - sequence of samples
• clusters - number of clusters
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of convergence test

See more in SMILE doc

mec clj

(mec data max-clusters radius)

(mec data dist max-clusters radius)

Nonparametric Minimum Conditional Entropy Clustering algorithm.

Input:

• data - sequence of samples
• dist (optional) - distance method, default :euclidean
• max-clusters - maximum number of clusters
• radius - the neighborhood radius

See more in SMILE doc

outlier-id clj

Id of the cluster which contain outliers.

predict clj

(predict cluster in)

Predict cluster for given vector

regroup clj

(regroup {:keys [clustering data representatives sizes]})

Transform clustering result into list of clusters as separate maps.

Every map contain:

• :key - cluster id or :outliers
• :data - samples which belong to the cluster
• :representative - centroid/medoid or average vector if the former is not available
• :size - size of cluster

Representative is always a n-dimensional sequence even if input is a list of numbers.

Empty clusters are skipped.

spectral clj

(spectral data clusters sigma)

(spectral data clusters samples sigma)

(spectral data clusters sigma max-iters tolerance)

(spectral data clusters samples sigma max-iters tolerance)

Spectral clustering

Input:

• data - sequence of samples
• clusters - number of clusters
• samples (optional) - number of random samples for Nystrom approximation
• sigma - width parameter for Gaussian kernel
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of k-means convergence test

See more in SMILE doc

x-means clj

(x-means data clusters)

(x-means data clusters max-iter)

(x-means data clusters max-iter tolerance)

X-Means

Input:

• data - sequence of samples
• clusters - number of clusters
• max-iter (optional) - maximum number of iterations
• tolerance (optional) - tolerance of convergence test

See more in SMILE doc