K-Medoids

hanaml.KMedoid is a R wrapper for SAP HANA PAL KMedoids algorithm.

hanaml.KMedoid(
  data,
  key,
  features = NULL,
  n.clusters,
  init = NULL,
  max.iter = NULL,
  tol = NULL,
  thread.ratio = NULL,
  distance.level = NULL,
  minkowski.power = NULL,
  category.weights = NULL,
  normalization = NULL,
  categorical.variable = NULL
)

Arguments

data	DataFrame DataFrame containting the data.
key	character Name of the ID column.
features	character or list of characters, optional Names of features columns. If is not provided, it defaults to all non-key columns of data.
n.clusters	integer Number of groups.
init	character, optional Controls how the initial centers are selected: "first.k": First k observations. "replace": Random with replacements. "no.replace": Random without replacements. "patent":Patent of selecting the init center (US 6,882,998 B1). Defaults to "patent".
max.iter	integer, optional Maximum number of iterations. Defaults to 100.
tol	double, optional Threshold for exiting the iteration. Defaults to 1e-6.
thread.ratio	double, optional Controls the proportion of available threads that can be used by this function. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads. Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored. Defaults to 0.
distance.level	character, optional Specifies how to compute the distance between the item and the cluster center. "manhattan" "euclidean" "minkowski" "chebyshev" "cosine" Defaults to "euclidean".
minkowski.power	double, optional When Minkowski distance is used, this parameter controls the value of power. Only valid when distance.level is 'minkowski'. Defaults to 3.0.
category.weights	double, optional Represents the weight of category attributes. Defaults to 0.707.
normalization	character, optional Specifies the normalization type: 'no': no normalization. 'l1.norm': For each point X = (x1,x2,...,xn), the normalized value will be X' = (x1/S,x2/S,...,xn/S), where S = |x1|+|x2|+...|xn|. min.max: Yes, for each column C, get the min and max value of C, and then C[i] = (C[i]-min)/(max-min). Defaults to "no".
categorical.variable	character or list/vector of characters, optional Indicates features should be treated as categorical variable. The default behavior is dependent on what input is given: "VARCHAR" and "NVARCHAR": categorical "INTEGER" and "DOUBLE": continuous. VALID only for variables of "INTEGER" type, omitted otherwise. No default value.

Value

A "KMedoid" object with the following attributes:

• labels : DataFrame
  Label assigned to each sample.

• cluster.centers : DataFrame
  Coordinates of cluster centers.

Details

The K-Medoids clustering algorithm partitions n observations into K clusters according to their nearest cluster center. It uses medoids to calculate cluster centers. The K-Medoids algorithm is more robust in regards to noise and outliers.

Examples

Input DataFrame data:

> data$Collect()
     ID  V000 V001  V002
1    0   0.5    A   0.5
2    1   1.5    A   0.5
3    2   1.5    A   1.5
4    3   0.5    A   1.5
5    4   1.1    B   1.2
......
16  15  15.5    D   0.5
17  16  16.5    D   0.5
18  17  16.5    D   1.5
19  18  15.5    D   1.5
20  19  15.7    A   1.6

Call the function:

> kmed <- hanaml.KMedoid(data = data,
                         key = "ID",
                         n.clusters = 4,
                         init = "first_k",
                         max.iter = 100,
                         tol = 1.0E-6,
                         thread.ratio = 0.3,
                         distance.level = "Euclidean",
                         category.weights = 0.5)

Output:

> kmed$cluster.centers$Collect()
   CLUSTER_ID  V000 V001  V002
1           0   1.5    A   1.5
2           1  15.5    D   1.5
3           2  15.5    C  16.5
4           3   1.5    B  16.5

>dkmed$labels$Collect()
   ID CLUSTER_ID  DISTANCE
1   0          0 1.4142136
2   1          0 1.0000000
3   2          0 0.0000000
4   3          0 1.0000000
......
17 16          1 1.4142136
18 17          1 1.0000000
19 18          1 0.0000000
20 19          1 0.9307136