Transaction Encoding Algorithm (TEA) for Distributed Data

Transaction Encoding Algorithm (TEA) for Distributed Data

Analysis of huge datasets has been a major concern in almost all areas of technology in the past decade and the role of data mining has become so crucial as a result of this crisis. As the data sizes in these datasets increase, from gigabytes to terabytes or even larger the complexity in collecting and warehousing these massive dataset as such in a single site is practically impossible as it ma...

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Distributed Genetic Algorithm to Big Data Clustering A Novel Distributed Encoding Techniques

Clustering algorithms have emerged as a powerful learning tool to accurately analyze the massive amount of data generated by current applications and smart technologies. Precisely, their main objective is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a wide and diverse body of knowledge in the are...

Transaction Management for Distributed Multidatabases

Distributed Encoding Algorithm for Source Localization in Sensor Networks

We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to attain a certain source localization accuracy. We propose a distributed encoding algorithm ...