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1. GDCluster: A General Decentralized Clustering Algorithm
ABSTRACT:
A General Distributed Clustering algorithm (GDCluster) is proposed and instantiated with two
popular partition based and density-based clustering methods. Using gossip-based
communication the nodes gradually build a summarized view of the data set by continuously
exchanging information on data items and data representatives. GDCluster can cluster a data set
which is dispersed among a large number of nodes in a distributed environment. It can handle
two classes of clustering, namely partition-based and density based. GDCluster is able to achieve
a high-quality global clustering solution, which approximates centralized clustering.
EXISTING SYSTEM:
The existing system considers the clustering of large datasets distributed over a network of
computational units using a decentralized K-means algorithm. To obtain the same codebook at
each node of the network, the system uses a gossip aggregation protocol where only small
messages are exchanged. The algorithm with a centralized K-means provided a bound on the
number of small messages each node has to send is met.
DISADVANTAGES:
 Require a central site to coordinate execution rounds, and/or merge local models.
 It does not avoid global message flooding.
 Lack of efficient solutions for adaptability in dynamic settings, which introduces
significant challenges for applying the algorithms in large-scale real-world networks
 This approach limits nodes to finding the same number of clusters.

2. PROPOSED SYSTEM:
We propose a GDCluster; it can cluster a data set which is dispersed among a large number of
nodes in a distributed environment. It can handle two classes of clustering, namely partition-
based and density based, while being fully decentralized and asynchronous. Our system dealing
with dynamic data and evolving the clustering model and Empowering nodes to construct a
summarized view of the data, to be able to execute a customized clustering algorithm
independently. Execute weighted clustering algorithms to build the clustering models. A
distributed K-means clustering algorithm for P2P networks in which nodes communicate with
their immediate neighbors. Each node is required to store history of cluster centroids per each K-
mean iteration.
ADVANTAGES:
 Scalable and efficient clustering with efficient transmission costs.
 Improves the overall clustering accuracy.