English

Abstract

This study predicts the best supervised learning method of clustering techniques in fuzzy back propagation network(FBPN). Image processing algorithms are used to extract the information and patterns derived by process. Classification are done using predictive model of fuzzy technique of back propagation algorithm The values of the features are evaluated by FBPN algorithm. Keywords— Clustering, Fuzzy, Back Propagation, Neural Networks I.INTRODUCTION Levinski, et al., (2009), describes the approach for correcting the segmentation errors in 3D modeling space, implementation, principles of the proposed 3D modeling space tool and illustrates its application. Paragios, et al., (2003), introduces a knowledge based constraints, able to change the topology, capture local deformations, surface to follow global shape consistency while preserving the ability to capture using implicit function. Suri, et al., (2002), an attempt to explore geometric methods, their implementation and integration of regularizers to improve robustness of independent propagating curves/surfaces. Yuksel, et al., (2006), reveals the 100% classification accuracy of carotid artery Doppler signals using complex-values artificial neural network. Wendelhag, et al., (1991, 1997) results shows variations secondary to subjective parameters when manual measurement methods are employed. A thorough computerized system is necessary to evaluate the pattern recognition using clustering techniques. Our proposed method acts as a tool to predict the same patterns in data effectively and efficiently with less time and less memory allocation. II.CLUSTERING Clustering is a technique for partitioning a group of images into meaningful disjoint subgroups. Images that are similar to each other group themselves into a single cluster. All the images in a subgroup are similar to each other. At the same time, the images across the clusters are different. Cluster analysis is different from classification. Clustering is an example of unsupervised learning where there is no idea about the classes or clusters prior to clustering. Some of the important topics to be discussed with respect to clustering algorithms are as follows: 1. Method for finding the similarities and dissimilarities of the images 2. Categorization of clustering algorithms 3. Evaluation of clustering algorithms Hierarchical clustering Hierarchical clustering techniques are based on the use of a proximity matrix indicating the similarity between every pair of data points to be clustered. The end result is a tree of clusters representing the nested group of patterns and similarity levels at which groupings change. The resulting clusters are always produced as the internal nodes of the tree, while the root node is reserved for the entire dataset and leaf nodes are for individual data samples. The clustering methods differ in regard to the rules by which two small clusters are merged or a large cluster is split. Agglomerative methods employ the following