okm.RdClusters data using the OKM (Overlapping K-Means) clustering algorithm.
okm(x, centers, iter.max = 10, nstart = 1, trace = FALSE, method = "euclid")A numeric data matrix or data frame containing the data to be clustered.
Either a positive integer indicating the number of clusters to create or a matrix of pre-initialized cluster centers.
Maximum number of iterations allowed for the clustering algorithm (default is 10).
Number of random initializations to find the best result (default is 1).
Logical value indicating whether to display the progress of the algorithm (default is `FALSE`).
A string specifying the distance metric to use; options are 'euclid' (Euclidean distance) or 'manhattan' (Manhattan distance) (default is "euclid").
A list containing the clustering results, including: - `cluster`: Matrix indicating the cluster assignments for each data point. - `centers`: The final cluster centers. - `tot.withinss`: Total within-cluster sum of squares. - `overlaps`: The measure of overlap among clusters.
okm(iris[, -5], 3)
#> OKM clustering with 0 clusters of sizes:
#>
#> Cluster centers:
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,] 4.570596 2.502337 2.146500 0.4472413
#> [2,] 5.325741 3.990748 1.171523 0.1606126
#> [3,] 6.615954 2.966835 5.451116 1.9291072
#>
#> Clustering matrix:
#> [,1] [,2] [,3]
#> [1,] 1 1 0
#> [2,] 1 1 0
#> [3,] 1 1 0
#> [4,] 1 1 0
#> [5,] 1 1 0
#> [6,] 0 1 0
#> [7,] 1 1 0
#> [8,] 1 1 0
#> [9,] 1 1 0
#> [10,] 1 1 0
#> [11,] 0 1 0
#> [12,] 1 1 0
#> [13,] 1 1 0
#> [14,] 1 1 0
#> [15,] 0 1 0
#> [16,] 0 1 0
#> [17,] 0 1 0
#> [18,] 1 1 0
#> [19,] 0 1 0
#> [20,] 0 1 0
#> [21,] 1 1 0
#> [22,] 1 1 0
#> [23,] 1 1 0
#> [24,] 1 1 0
#> [25,] 1 1 0
#> [26,] 1 1 0
#> [27,] 1 1 0
#> [28,] 1 1 0
#> [29,] 1 1 0
#> [30,] 1 1 0
#> [31,] 1 1 0
#> [32,] 1 1 0
#> [33,] 0 1 0
#> [34,] 0 1 0
#> [35,] 1 1 0
#> [36,] 1 1 0
#> [37,] 0 1 0
#> [38,] 1 1 0
#> [39,] 1 1 0
#> [40,] 1 1 0
#> [41,] 1 1 0
#> [42,] 1 0 0
#> [43,] 1 1 0
#> [44,] 1 1 0
#> [45,] 1 1 0
#> [46,] 1 1 0
#> [47,] 0 1 0
#> [48,] 1 1 0
#> [49,] 0 1 0
#> [50,] 1 1 0
#> [51,] 0 0 1
#> [52,] 0 0 1
#> [53,] 0 0 1
#> [54,] 1 0 1
#> [55,] 0 0 1
#> [56,] 1 0 1
#> [57,] 0 0 1
#> [58,] 1 0 1
#> [59,] 0 0 1
#> [60,] 1 0 1
#> [61,] 1 0 1
#> [62,] 1 0 1
#> [63,] 1 0 1
#> [64,] 0 0 1
#> [65,] 1 0 1
#> [66,] 0 0 1
#> [67,] 1 0 1
#> [68,] 1 0 1
#> [69,] 1 0 1
#> [70,] 1 0 1
#> [71,] 0 0 1
#> [72,] 1 0 1
#> [73,] 0 0 1
#> [74,] 1 0 1
#> [75,] 1 0 1
#> [76,] 0 0 1
#> [77,] 0 0 1
#> [78,] 0 0 1
#> [79,] 1 0 1
#> [80,] 1 0 1
#> [81,] 1 0 1
#> [82,] 1 0 1
#> [83,] 1 0 1
#> [84,] 0 0 1
#> [85,] 1 0 1
#> [86,] 1 0 1
#> [87,] 0 0 1
#> [88,] 1 0 1
#> [89,] 1 0 1
#> [90,] 1 0 1
#> [91,] 1 0 1
#> [92,] 1 0 1
#> [93,] 1 0 1
#> [94,] 1 0 1
#> [95,] 1 0 1
#> [96,] 1 0 1
#> [97,] 1 0 1
#> [98,] 1 0 1
#> [99,] 1 0 1
#> [100,] 1 0 1
#> [101,] 0 0 1
#> [102,] 0 0 1
#> [103,] 0 0 1
#> [104,] 0 0 1
#> [105,] 0 0 1
#> [106,] 0 0 1
#> [107,] 1 0 1
#> [108,] 0 0 1
#> [109,] 0 0 1
#> [110,] 0 0 1
#> [111,] 0 0 1
#> [112,] 0 0 1
#> [113,] 0 0 1
#> [114,] 0 0 1
#> [115,] 0 0 1
#> [116,] 0 0 1
#> [117,] 0 0 1
#> [118,] 0 0 1
#> [119,] 0 0 1
#> [120,] 0 0 1
#> [121,] 0 0 1
#> [122,] 0 0 1
#> [123,] 0 0 1
#> [124,] 0 0 1
#> [125,] 0 0 1
#> [126,] 0 0 1
#> [127,] 0 0 1
#> [128,] 0 0 1
#> [129,] 0 0 1
#> [130,] 0 0 1
#> [131,] 0 0 1
#> [132,] 0 0 1
#> [133,] 0 0 1
#> [134,] 0 0 1
#> [135,] 0 0 1
#> [136,] 0 0 1
#> [137,] 0 0 1
#> [138,] 0 0 1
#> [139,] 0 0 1
#> [140,] 0 0 1
#> [141,] 0 0 1
#> [142,] 0 0 1
#> [143,] 0 0 1
#> [144,] 0 0 1
#> [145,] 0 0 1
#> [146,] 0 0 1
#> [147,] 0 0 1
#> [148,] 0 0 1
#> [149,] 0 0 1
#> [150,] 0 0 1
#>
#> Total within-cluster sum of squares:
#> [1] 85.15571
#>
#> Overlapping measure:
#> [1] 1.486667
#> Available components:
#> [1] "cluster" "centers" "tot.withinss" "overlaps"