scholarly journals Data Mining and Cloud Computing

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Robert Vrbić
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Warehouses ◽  
Cloud Data ◽  
Big Data Mining ◽  
New Knowledge ◽  
Flexible Infrastructure

Cloud computing provides a powerful, scalable and flexible infrastructure into which one can integrate, previously known, techniques and methods of Data Mining. The result of such integration should be strong and capacitive platform that will be able to deal with the increasing production of data, or that will create the conditions for the efficient mining of massive amounts of data from various data warehouses with the aim of creating (useful) information or the production of new knowledge. This paper discusses such technology - the technology of big data mining, known as Cloud Data Mining (CDM).

scholarly journals A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

2021 ◽  
Vol 23 (06) ◽  
pp. 29-35
Author(s):  
A. Vaitheeswari ◽  
◽  
Dr. N. Krishnaveni ◽  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Virtual Machines ◽  
Big Data Analysis ◽  
Correspondence Problem ◽  
Weighted Norm ◽  
Matrix Structure ◽  
Method Of Multipliers ◽  
Big Data Mining

Matrix structure was one of the most important devices for finding data from big data. Here you’ll find data produced by current applications using cloud computing. However, moving big data using such a system in a performance computer or through virtual machines is still inefficient or impossible. Furthermore, big data is often gathered data from a variety of data sources and stored on a variety of machines using scheduling algorithms. As a result, such data usually bear solid shifted commotion. Growing circulated matrix deterioration is necessary and beneficial for big data analysis. Such a plan should have a good chance of succeeding. Represent the diverse clamor and deal with the correspondence problem in a disseminated manner. In order to do this, we used a Bayesian matrix decay model (DBMD) for big data mining and grouping. Only three approaches to disseminated computation are considered: 1) accelerate slope drop, 2) alternating path method of multipliers (ADMM), and 3) observable derivation. We look at how these approaches could be mixed together in the future. To deal with the commotion’s heterogeneity, we suggest an ideal module weighted norm that reduces the assessment’s differentiation. Finally, a comparison was made between these approaches in order to understand the differences in their outcomes.

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

Cloud Computing and Big Data

2014 ◽  
pp. 118-132
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Association Rules ◽  
Big Data Analysis ◽  
Center Problem ◽  
Privacy Concerns ◽  
Numerical Examples ◽  
Big Data Mining

This chapter aims at exploring the intersection of cloud computing with big data. The big data analysis, mining, and privacy concerns are discussed. First, this chapter deals with the software framework, MapReduce™ that is commonly used for performing Big Data Analysis in the clouds. In addition, some of the most used techniques for performing Big Data Mining are detailed. For instance, Clustering, Co-Clustering, and Association Rules are described in detail. In particular, the k-center problem is described while with reference to the association rules beyond the basic definitions, the Apriori Algorithm is outlined and illustrated by some numerical examples. These techniques are also described with reference to their versions based on MapReduce. Finally, the description of some real applications conclude the chapter.

Key Methodologies for Designing Big Data Mining Platform Based on Cloud Computing

2016 ◽  
pp. 190-196
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Mining Area ◽  
Mining System ◽  
Big Data Mining ◽  
Key Technologies ◽  
Data Mining System

Cloud Computing plays a big function in the in data mining area of numerous sectors in today's culture. Building the data mining system based upon cloud computing is useful to accomplish effective data mining This paper evaluates the basic architecture of the big data mining platform based on cloud computing and the key technologies for its building on the basis of relevant concepts of cloud computing and also data mining.

Sign in / Sign up

Export Citation Format

Share Document