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@article{145529,
        author = {G.Amani and k.venkata ramana},
        title = {Relative study of Prediction KNN Algorithm Using Normalization Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {856-859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145529},
        abstract = {The task of classifying a set of documents into different categories from group of sets. Here K- Nearest Neighbors algorithm is used. In this algorithm is is mainly used for data mining and pattern recognition and machine learning because its is very easy to understand and this perfoermance is good. It is non-parametric technique for regression and catagorization. KNN (K- Nearest Neighbors algorithm) is popular method to categorize the dataset. This paper is concerned with the comparative study or analysis of K-Nearest neighbor algorithm under different normalization techniques and different values of K. For the comparative analysis, we have used “IRIS” Dataset. To measure accuracy, Here we are used two normalization techniques that are Z-Score Normalization and Min-Max Normalization. Using these techniques accuracy and performance will be increases compared other techniques using data sets. Also, we have computed the average prediction efficiency of K-nearest neighbor algorithm using the two normalization techniques and concluded the one technique with the highest efficiency. },
        keywords = {Data mining, Prediction KNN (K- Nearest Neighbors algorithm), Z-Score and Min-Max Normalization techniques.},
        month = {},
        }