Data classification using local probability and statistical hypothesis theory

In this paper, we propose a new classification method using local probability and statistical hypothesis theory. To separate the test data, we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. To evaluate, the proposed classification method is compared to the conventional fuzzy c-mean method and k-means algorithm. The simulation results show more accuracy than results of fuzzy c-mean method and k-means algorithm.