Minimum k-cluster algorithm for hierachical reinforcement learning;

Research on a method for building up a patent map based on k-means clustering algorithm

In order to obtain better clustering results,after analyzing the advantages and disadvantages of hierarchical and k-means clustering algorithms,a new algorithm which combines the advantages of hierarchical and k-means clustering algorithms is proposed.

The complexity of time and spatial is becoming the difficulty of K-Means clustering algorithm while it deals with the huge amounts of data sets.

The learning of this method is divided into two processes,state space learning using K-means clustering algorithm for adaptive discretization of continuous states and policy learning using Sarsa algorithm for finding optimal policy.

Application of the improved K-means clustering algorithm in support vector machine;

According to the characters of the images, the algorithm separated image into several regions by K-means clustering algorithm, and each region is equalized respectively within their gray levels.

Incremental Clustering Algorithm Based on K-Medoids;