Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. ...

Network clustering can facilitate data discovery and peerlookup in peer-to-peer systems. In this paper, we design a distributed network clustering protocol, called SCM-based Distributed Clustering (SDC), for peer-to-peer networks. In this protocol, clustering is dynamically adjusted based on Scaled Coverage Measure (SCM), a practical clustering accuracy measure. By exchanging messages with neig...

Often, the information is sensitive or private in nature and these sensitive data when mined violates the privacy of the individuals. Privacy preserving data mining (PPDM) mines the data but intends to preserve the privacy of susceptible data without ever actually seeing it. This paper recaps the important techniques in PPDM like anonymization, perturbation and cryptography. Nowadays, data mini...

Clustering is a significant mechanism used in Wireless Sensor Networks in order to have an efficient energy balance which is inevitable to prolong the lifetime. The concept of unequal clustering has proved to be an effective method for load balancing and thereby reducing hotspot issues in the energy constrained wireless sensor networks. This paper proposes an energy efficient clustering mechani...

Many parallel and distributed clustering algorithms have already been proposed. Most of them are based on the aggregation of local models according to some collected local statistics. In this paper, we propose a lightweight distributed clustering algorithm based on minimum variance increases criterion which requires a very limited communication overhead. We also introduce the notion of distribu...

This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by [13], we reduce the problem of finding a clustering with low cost to the problem of finding a coreset of small size. We p...

In this paper, we will propose a distributable clustering algorithm, called Distributed-GridMST (D-GridMST), which deals with large distributed spatial databases. D-GridMST employs the notions of multi-dimensional cube to partition the data space involved and uses density criteria to extract representative points from spatial databases, based on which a global MST of representatives is construc...

Large-scale clustering of data points in metric spaces is an important problem in mining big data sets. For many applications, we face explicit or implicit size constraints for each cluster which leads to the problem of clustering under capacity constraints or the “balanced clustering” problem. Although the balanced clustering problem has been widely studied, developing a theoretically sound di...

Distributed implementations are crucial in speeding up large scale machine learning applications. gradient descent (GD) is widely employed to parallelize the task by distributing dataset across multiple workers. A significant performance bottleneck for per-iteration completion time distributed synchronous GD straggling Coded computation techniques have been introduced recently mitigate straggle...