Abstract-Classification is the technique of identifying and assigning individual quantities to a group or a set. In pattern recognition, K-Nearest Neighbors algorithm is a non-parametric method for classification and regression. The K-Nearest Neighbor (kNN) technique has been widely used in data mining and machine learning because it is simple yet very useful with distinguished performance. Classification is used to predict the labels of test data points after training sample data. Over the past few decades, researchers have proposed many classification methods, but still, KNN (K-Nearest Neighbor) is one of the most popular methods to classify the data set. The input consists of k closest examples in each space, the neighbors are picked up from a set of objects or objects having same properties or value, this can be considered as a training dataset. In this paper, we have used two normalization techniques to classify the IRIS Dataset and measure the accuracy of classification using Cross-Validation method using R-Programming. The two approaches considered in this paper are -Data with Z-Score Normalization and Data with Min-Max Normalization.

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The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is extremely difficult without any prior knowledge about the object that is being extracted from the scene.We propose a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which will provide contextual information regarding the objects to be segmented. We note that traditional segmentation can then be viewed as a filter operating on the image independently of the classifier, much like the filter methods for feature selection. Our motivation for integrating segmentation and classification follows the wrapper methods of feature selection. In the wrapper methods for feature selection, the classifier is an integral part of the selection process and serves as the metric to decide the best feature set. In the same way, we wrap the segmentation and classification together, and use the classification accuracy as the metric to determine the best segmentation. We show the performance of wrapper-based segmentation on real-world and complex images of automotive vehicle occupants.

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