Posted by santa on April 16, 20011
In Reply to Re: need help ?? how implementation fuzzy c-means posted by Math-Man on March 27, 20011: : help me please...!!!!!!
: First a short intro for most readers who never heard of "fuzzy c-means"...
: Fuzzy C-Means (FCM) clustering allows a set of data-points to belong to two or more clusters. Each point has a degree of belonging to the various clusters (between 0 and 1, as in fuzzy logic). Thus, points on the edge of a cluster, may be in the cluster to a lesser degree than points in the center of cluster.
: Clustering is a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, information retrieval, and bio-informatics.
: FCM partitioning is carried out through an iterative method, where an algorithm is repeated until a calculated value becomes smaller than a given "error value" e (between 0 and one). The smaller e, the greater the precision and the more steps will be needed.
: For each point X we have a coefficient Uk(x) giving the degree of being in the K-th cluster. Usually, the sum of those coefficients for any given X is defined to be 1.
: With FCM, the "centroid" of a cluster is the mean of all points, weighted by their degree of belonging to the cluster ( this degree is related to the inverse of the distance to the cluster center).
: The FCM algorithm is carried out as follows:
: 1. Choose a number of clusters.
: 2. Assign randomly to each point coefficients for being in the clusters.
: 3. Repeat until the algorithm has converged (that is, the coefficients' change between two iterations is no more than e, the given sensitivity threshold):
: 3.1 Compute the centroid for each cluster.
: 3.2 For each point, compute its coefficients of being in the clusters.
: Gimme a few days to come up with an example and some code.