A Data Classification Method Using Genetic Algorithm and K-Means Algorithm with Optimizing Initial Cluster Center

Aiming at the problems of the classical data classification method, this paper proposes a method using genetic algorithm and K-means algorithm to classify data. In order to improve the effectiveness of data analysis, considering that the classical K-means algorithm is easy to be influenced by the initial cluster center with random selection, this paper improves the K-means algorithm by using the method of optimizing the initial cluster center. This paper first uses the sorted neighborhood method (SNM) to preprocess the data, and then the K-means algorithm is used to cluster data. In order to improve the accuracy of the K-means algorithm, this paper optimizes the initial cluster center, and unifies the genetic algorithm for the data dimensionality reduction. The experimental results show that the proposed method has higher classification accuracy than the classical data classification method has.