Clustering by density Estimation and mode seeking

• Clusters are viewed as regions of the pattern space in which the patterns are dense, separated by regions of low pattern density.
• Clusters can be identified by searching for regions of high density, called modes, in the pattern space.
• Each mode is associated with the cluster center.
• Each pattern is assigned to the cluster with the closest center.
• Probability density estimate at a point x is proportional to the number of patterns falling in a small region around x. The choice of is critical when n is small.
• Simplest way to identify modes is to construct a histogram by partitioning the pattern space into a number of non-overlapping regions. Number of patterns should be large to get a good estimate.