Introduction to Pattern Recognition and Clustering Techniques Technical Seminar Topic:

A pattern is a sequential arrangement. It is identified by the order of elements made by intrinsic nature of elements. The process of analyzing, discovering and understanding patterns that are related to the image called as image identification of analysis.

Arrangement of descriptors is known as pattern. It is a class of a family that share common properties. They are represented by w1, w2, w3,w4…..wm. Where m is the numbers of classes. Pattern is a structural description of an object.

Neural network: it consists of background and training pattern. They are the perception of two pattern classes. It has an activation function that looks for fixed increment of correction rule. This is also known as perception theorem.

K-means algorithms: This algorithm converges. It is used because many times user assigns value before using starting process. It is important that two different initial partition results into same clustering. It cannot be used in non-convex or elongated clusters.

Hierarchical clustering technique: they are generally of two types: agglomerative and divisive. When the numbers of clusters used are same to number of points then two clusters merged to result in to one cluster. Then it is called agglomerative. When the number of cluster is used is one and at every stage it is divided into two clusters and at last stage it results into n clusters.

K-means is an example of non-hierarchical technique. In case number of clusters is known to us and we stop the technique then the number of clusters obtain as desired number, and if they are unknown then we have to find the number of clusters in some other way after complete formation. 

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