Data Preprocessing With K Nearest Neighbor, Normalization In Big Data - K. Sathesh Kumar*, S. Ramkumar, P.Nagaraja, A. Robert Singh (International Journal of Advanced Science and Technology, 2020)
The proposed MapReduce framework works more effectively in big data. The big data concernlarge-volume, complex, growing data sets with multiple, autonomous sources. This may leadproblems such as noisy data, missing data and redundant data because the big data is collectedfrom multiple systems or sources which affects the decision making for data mining with big datain this work the k nearest neighbor algorithm is used to calculate the missing values. Assumeeach feature of training data sets has a distinct dimension in some scope, and take anobservation value for its feature coordinates in that dimension, then acquire some set of pointsfrom space. Then assume the identical of two points would be the distance between them in thisspace depends on some metric. This method is used to gain proper dataset and it increases dataquality for further processes.