动态分布k-均值集群系统

Distributed K-means Algorithm K-means clustering is a well-known and well-studied exploratory data analysis technique. The standard version assumes that all data are available at a single location. The system deployed in this project implements a dynamic distributed k-means algorithm which has been described in the section 5 of the paper “Approximate Distributed K-Means Clustering over a Peer-to-Peer Network”. It takes a completely decentralized approach, where peers (nodes) only synchronize with their immediate neighbors. The result of this distributed k-means algorithm has been tested to achieve very good accuracy (more than 97%, mentioned in the section 6.5.1 of the paper) compared to centralized k-means clustering. Goal Aiming to realize a distributed kmeans clustering algorithm which allows dynamic change of nodes. Still in the process of changing from static clustering to dynamic one. There are three steps: 1) allowing the

dk_means
peer.py
server.py
peer_01.py
peer_02.py
results
.svn
distributed_kmeans.py
.project
.pydevproject
interface.py