7- K-MEANS CLUSTERING ALGORITHM
In K-means clustering approach, we partition the set of input patterns S into a set of K partitions. The method is based on the identification of the centroids of each of the K clusters. Thus, instead of computing the pairwise interpattern distances between all the patterns in all the clusters, here the distances may be computed only from the centroids. The method thus essentially boils down to searching for a best set of K centroids of the clusters as follows:
Step 1: Select K initial cluster centers C1, C2,. . . , CK.
Step 2: Assign each pattern X in S to a cluster Ci, (1 <= i <= K ) , whose centroid is nearest to pattern X.
Step 3: Recompute the centroids in each cluster Ci, (1 <= j <= K.
Step 4: Jump to step 2, until convergence is achieved.
8- SYNTACTIC PATTERN CLASSIFICATION
It may be noted that there exists an inherent structure inside a pattern and there is a positive interrelationship among the primitive elements which form a pattern. The limitations of decision theoretic pattern classification techniques lie in their incapability to articulate this interrelationship of the pattern substructures. This has led to a new era of structural or syntactic pattern recognition. The interrelationship between pattern elements called primitives and the articulated description of a pattern in terms of such relations provide a basis of structural or linguistic approach to pattern recognition.
In syntactic pattern recognition each pattern is characterized by a string of primitives and the classification of a pattern in this approach is based on analysis of the string with respect to the grammar defining that pattern class.
The syntactic approach to pattern recognition involves a set of processes,
1. Selection and extraction of a set of primitives (segmentation problem)
2. Analysis of pattern description by identification of the interrelationship among the
primitives
3. Recognition of the allowable structures defining the interrelationship between the
pattern primitives