Study of data ming classification based on genetic algorithm
Xiaofeng Li, Chan Xin, Li Yang · 2010
In view of genetic superiority in data mining algorithms, this paper combines the genetic algorithm and K-means algorithm and presents a genetic algorithm based k-means clustering algorithm and the algorithm to improve genetic clustering algorithm clustering using variable length actual real number of cluster center, and design a new crossover and mutation operators and the introduction of is widely used cluster validity index DB-Index as the target function, it not only better solve the K-means clustering algorithm, the number of clusters is difficult to determine the initial value of sensitivity and defects such as easy to fall into local optimum, and the algorithm efficiency and accuracy of the algorithm are greatly improved and compared with previous algorithms.