FindClusters

FindClusters(list-of-data-points, k)

Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm. Create k number of clusters to split the list-of-data-points into.

FindClusters(list-of-data-points, eps, minPts, Method->"DBSCAN", DistanceFunction->EuclideanDistance)

Use the density-based spatial clustering of applications with noise (DBSCAN) algorithm as clustering Method. eps is the maximum radius of the neighborhood to be considered. minPts is the minimum number of points needed for a cluster. The DistanceFunction defines the function which should be used to measure the distance between 2 points.

See:

• Wikipedia - K-means++
• Wikipedia - K-means clustering
• Wikipedia - DBSCAN

Examples

>> FindClusters({1, 2, 3, 4, 5, 6, 7, 8, 9})
{{1.0,2.0,3.0},{6.0,7.0,8.0,9.0},{4.0,5.0}}

>> FindClusters({{83.08303244924173,58.83387754182331},{45.05445510940626,23.469642649637535},{14.96417921432294,69.0264096390456},{73.53189604333602,34.896145021310076},{73.28498173551634,33.96860806993209},{73.45828098873608,33.92584423092194},{73.9657889183145,35.73191006924026},{74.0074097183533,36.81735596177168},{73.41247541410848,34.27314856695011},{73.9156256353017,36.83206791547127},{74.81499205809087,37.15682749846019},{74.03144880081527,37.57399178552441},{74.51870941207744,38.674258946906775},{74.50754595105536,35.58903978415765},{74.51322752749547,36.030572259100154},{59.27900996617973,46.41091720294207},{59.73744793841615,46.20015558367595},{58.81134076672606,45.71150126331486},{58.52225539437495,47.416083617601544},{58.218626647023484,47.36228902172297},{60.27139669447206,46.606106348801404},{60.894962462363765,46.976924697402865},{62.29048673878424,47.66970563563518},{61.03857608977705,46.212924720020965}}, 2.0, 5, Method->"DBSCAN", DistanceFunction->EuclideanDistance)

{{{73.5319,34.89615},{73.28498,33.96861},{73.45828,33.92584},{73.96579,35.73191},{74.00741,36.81736},{73.41248,34.27315},{73.91563,36.83207},{74.50755,35.58904},{74.51323,36.03057},{74.81499,37.15683},{74.03145,37.57399},{74.51871,38.67426}},{{59.27901,46.41092},{59.73745,46.20016},{58.81134,45.7115},{58.52226,47.41608},{58.21863,47.36229},{60.2714,46.60611},{60.89496,46.97692},{61.03858,46.21292},{62.29049,47.66971}}}

Related terms

BinaryDistance, BrayCurtisDistance, ChessboardDistance, CanberraDistance, CosineDistance, EuclideanDistance, ManhattanDistance, SquaredEuclideanDistance

Implementation status

• ✅ - full supported

Github

• Implementation of FindClusters