Clustering

• Partitioned-based clustering
  • k-means, k-median, fuzzy c-means
• Hierarchical clustering
  • Produces trees of clusters
  • Agglomerative, divisive
  • Advantages
    • It does not require the number of clusters to be specified.
    • Produces a dendrogram which helps with understanding the data.
  • Disadvantages
    • It can never undo any previous steps throughout the algorithm.
    • Sometimes difficult to identify the number of clusters by the dendrogram.
• Density-bassed clustering
  • Produces arbitrary shaped clusters
  • Locates regions of high density, and separates outliers
  • DBSCAN
    
    • Does not require specification of the number of clusters
• Time-series clustering by features 
  • Time-series clustering by features.
    • Raw data.
    • Autocorrelation.
    • Spectral density.
    • Extreme value behavior.
  • Model-based time series clustering.
    • Forecast based clustering.
    • Model with a cluster structure.
  • Time-series clustering by dependence.
• Clustering high dimensional data
  • Many clustering algorithms deal with 1-3 dimensions
  • These methods may not work well when the number of dimensions grows to 20
• Methods for clustering high dimensional data
  • Methods can be grouped into two categories
    • Subspace clustering
      • CLIQUE, ProClus, and bi-clustering approaches
    • Dimensionality reduction approaches
      • Spectral clustering and various dimensionality reduction methods
  • Clustering should not only consider dimensions but also attributes/features
    • Feature selection
    • Feature transformation
      • Principal component analysis, singular value decomposition