Optimal Classification/clustering

Many people consider clustering the most important unsupervised learning problem. Clustering deals with finding structure in a collection of unlabeled data.

Clustering has many practical applications.

Many clustering algorithms ( k-clustering algorithms ) require a human to specify ahead of time how many buckets (categories, labels, classes, etc.) to divide up the input into.

  • Median cut clustering -- perhaps the fastest clustering algorithm
  • K-means clustering -- a very fast clustering algorithm, with the disadvantage that it does not provide the same result with each run.
  • QT clustering algorithm
  • expectation-maximization algorithm
  • canopy clustering algorithm
  • constrained clustering
  • Fuzzy clustering by Local Approximation of MEmberships (FLAME clustering)
  • self-organizing map
  • vector quantization
  • Learning Vector Quantization

further readingEdit