KNN Technique for Analysis and Prediction of Temperature and Humidity Data
The paper investigates the data mining technique K-nearest neighbor resulting in a predictor for numerical series. The series experimented with come from the climatic data usually hard to forecast due to uncertainty. One approach of prediction is to spot patterns in the past, when it is known in advance what followed them and verify it on more recent data. If a pattern is followed by the same outcome frequently enough, it can be concluded that it is a genuine relationship. Because this approach does not assume any special knowledge or form of the regularities, the method is quite general applicable to other series not just climate.