Simulation on K-Means Optimum Clustering Mining Algorithm Based on Slope Classification

Abstract

The efficient data mining algorithm was researched in this paper, according to the massive data in the database, the efficiency and the fluency of the data mining should be attached much importance in the research. And yet at the same time, the precision of mining algorithm should be improved. Combing with the genetic algorithm and K-means clustering algorithm, an improved data mining algorithm was proposed. In the new algorithm, the slope factor was taken in advantage, then the phenomenon that the smaller classification caused the less optimum solution was avoided, and the defects of the two algorithms are offset. The mining simulation and experiment was taken based on the different databases with different sizes of data. Simulation result shows that the new algorithm based on the slope factor K-means clustering genetic method can solve the data mining problem for the large data base. The data mining result is much more precise than the traditional method. Research result shows the improved algorithm has predominant prospect in application, and it has good value in the engineering practice.