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.

• Periodical: Advanced Materials Research (Volumes 791-793)
• Pages: 1385-1388
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.791-793.1385
• Online since: September 2013
• Authors: Si Jin Zhou, Shou Jian Wu, Hao Jiang
• Keywords: Data Mining (DM), Genetic Algorithm (GA), K-Means Clustering Algorithm, Slope Factor

References

[1] Kim H A, Karp B. Autograph: Toward Automated Distributed Worm Signature Detection [D]. USA: Camegie Mellon University, (2004).

[2] YinX， HanJ，YangJ, eta1. Cross Mine Efficient Classification Across Multiple Database Relations [C]. Boston 2004: 399-410.

[3] HU Guangbo, HE Xibing, GAN Xinnian. arget Detection Algorithm Based on Fractional Fourier Transform Cumulants[J]. Torpedo Technology, 2011, 19(5): 344-348.

[4] Zhang Yi, Zhou Bingying, Hu Guangbo. Hardware System Design of Underwater Motor Pump Faults Diagnose Detector[J]. Computer & Digital Engineering, 2012, 40(11): 162-166.

[5] Das S, AbrahamA, KonarA. Partiele Swarm OPtimizationand Differential Evolution Algorithms: Teehnieal Analysis, AP Plicationsand Hyridization perspectices [J] Studiesin Computational Intelligenee 116 , l-38 (2008).