K-means Optimization Algorithm for Solving Clustering Problem
Jinxin Dong, Minyong Qi · 2009
The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal value. To solve such problems, a new clustering algorithm is proposed based on simulated annealing. The algorithm views the clustering as optimization problem, the bisecting K-means splits the dataset into k clusters at first, and then run simulated annealing algorithm using the sum of distances between each pattern and its centre based on bisecting K-means as the aim function. To avoid the shortcomings of simulated annealing such as long computation time and low efficiency, a new data structure named sequence list is given. The experiment result shows the feasibility and validity of the proposed algorithm.