Data Mining Meets Grid Computing: Time to Dance?

Grid computing is a new and quickly developmental calculation model with the development of Internet technology, focuses on integrating distributed, heterogeneous and idle computers from the Internet to be a service system with high performance. This paper gives a brief introduction of grid computing, and do research on the architecture and implementation of a data mining system based on grid computing, that is DMSGrid, a grid computing data mining applications, not only considers efficient parallel computing as a crucial aspect, but also takes into account dynamic resource configuration and provides an engine to execute the algorithm flow specified in an application.

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Decision tree construction for data mining on grid computing

IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004 ◽

10.1109/eee.2004.1287344 ◽

2004 ◽

Cited By ~ 1

Author(s):

Shu-Tzu Tsai ◽

Chao-Tung Yang

Keyword(s):

Data Mining ◽

Decision Tree ◽

Grid Computing ◽

Tree Construction

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The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

Entropy ◽

10.3390/e22121410 ◽

2020 ◽

Vol 22 (12) ◽

pp. 1410

Author(s):

Murad B. Khorsheed ◽

Qasim M. Zainel ◽

Oday A. Hassen ◽

Saad M. Darwish

Keyword(s):

Fault Tolerance ◽

Load Balancing ◽

Grid Computing ◽

Computing Time ◽

Index Structure ◽

Computational Grids ◽

Optimum Number ◽

Logical Network ◽

Proposed Model ◽

Cloud Of Points

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.

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Closed Frequent Itemsets Mining Based on It-Tree

Journal of Medical Informatics and Decision Making ◽

10.14302/issn.2641-5526.jmid-20-3424 ◽

2020 ◽

Vol 1 (2) ◽

pp. 44-52

Author(s):

Youssef Fakir ◽

Chaima Ahle Touate ◽

Rachid Elayachi ◽

Mohamed Fakir

Keyword(s):

Data Mining ◽

Association Rule ◽

Computing Time ◽

Frequent Itemsets ◽

Closed Frequent Itemsets ◽

Hidden Knowledge ◽

Closed Itemsets ◽

Frequent Itemsets Mining ◽

Direct Counting ◽

Very High

In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting & intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.

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