Clustering
Farthest First algorithm for clustering
Farthest first is an algorithm to choose the cluster centers in K-means clustering. It works by placing each cluster centre in turn at the point furthest from the existing cluster centres. This point must lie within the data area. This greatly speeds up the clustering in most cases since less reassignment and adjustment are needed.
Method parameters
- Data files
- Raw data files correspondent to the samples selected to bi in the projection plot.
- Colouring style
- The dots corresponding to every sample can be colored depending on the sample's parameter state or on the file.
- Peak measuring approach
- It can take two values: height or area. The projections will be calculated using one of this two values.
- Peaks
- Peaks that will be taken into account to create the projection plot.
- Visualization
- The visualization of the result of non hierarchical clustering algorithms can be performed using PCA or Sammon's projection
- Type of data
- It can take two values: Samples or variables. The clustering will be applied to one of this types of data.
- Algorithm
- Algorithm that will be used to cluster the data.
- Link type
- This parameters is only enable when the hierarchical clustering has been chosen. The distances between clusters is determined by the chosen linkage.
- Distance fuction
- This parameters is only enable when the hierarchical clustering has been chosen. The distances between points is determined by the chosen distance function.
- Number of groups
- The number of clusters has to be defined by the user in advance for some clustering algorithms. This parameter is available only when K-means or Farthest First algorithm are chosen.