A High Performance CPU-GPU Algorithm for Change Detection in Satellite Images Using Singular Values and Data Mining Techniques

2018 ◽  
Author(s):  
Quoc-Nam Tran ◽  
A. Aafaque
Keyword(s):  
Data Mining ◽  
Change Detection ◽  
High Performance ◽  
Satellite Images ◽  
Singular Values ◽  
Data Mining Techniques

Parallel, Distributed, and Grid-Based Data Mining

2011 ◽  
pp. 90-119
Author(s):  
Moez Ben HajHmida ◽  
Antonio Congiusta
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Life Sciences ◽  
Business Organizations ◽  
Data Mining Techniques ◽  
Software And Hardware ◽  
Mining Methods ◽  
High Performance Computers ◽  
Grid Based ◽  
Major Data

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.

scholarly journals DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES

2017 ◽  
Vol 37 (4) ◽  
pp. 750-759 ◽  
Author(s):  
Willyan R. Becker ◽  
Jerry A. Johann ◽  
Jonathan Richetti ◽  
Laíza C. DE A. Silva
Keyword(s):  
Data Mining ◽  
Satellite Images ◽  
Data Mining Techniques ◽  
Summer Crop

scholarly journals Data mining techniques on satellite images for discovery of risk areas

2017 ◽  
Vol 72 ◽  
pp. 443-456 ◽  
Author(s):  
Boukaye Boubacar Traore ◽  
Bernard Kamsu-Foguem ◽  
Fana Tangara
Keyword(s):  
Data Mining ◽  
Satellite Images ◽  
Data Mining Techniques ◽  
Risk Areas

Parallel, Distributed, and Grid-Based Data Mining

2012 ◽  
pp. 203-231 ◽  
Author(s):  
Moez Ben HajHmida ◽  
Antonio Congiusta
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Life Sciences ◽  
Business Organizations ◽  
Data Mining Techniques ◽  
Software And Hardware ◽  
Mining Methods ◽  
High Performance Computers ◽  
Grid Based ◽  
Major Data

Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data being collected and for the complexity of the analysis that need to be performed on it. Classic data mining techniques, developed for centralized sites, often reveal themselves inadequate, due to some unique characteristics of today’s data sources. In such cases, sequential approaches to data mining cannot provide for scalability, in terms of the data dimensionality, size, and runtime performance. Moreover, the increasing trend towards decentralized business organizations, distribution of users, software, and hardware systems magnifies the need for more advanced and flexible approaches and solutions. Life science is one of the application areas that best resemble such scenario. This chapter presents the state of the art about the major data mining techniques, systems and approaches. A detailed taxonomy is drawn by analyzing and comparing parallel, distributed and Grid-based data mining methods, with a particular focus on the exploitation of large and remotely dispersed datasets and/or high-performance computers.

scholarly journals Exploiting Data Mining Techniques For Improving the Efficiency of Time Series Data

2014 ◽  
pp. 284-293
Author(s):  
TARUN DHAR DIWAN ◽  
PRADEEP CHOUKSEY ◽  
R. S. THAKUR ◽  
BHARAT LODHI
Keyword(s):  
Data Mining ◽  
Time Series ◽  
High Performance ◽  
Time Series Data ◽  
Research Work ◽  
Visual Programming ◽  
Series Data ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Vantage Points

The research work in data mining has achieved a high attraction due to the importance of its applications This paper addresses some theoretical and practical aspects on Exploiting Data Mining Techniques for Improving the Efficiency of Time Series Data using SPSS-CLEMENTINE. This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs. In this paper, we propose utilizes the famous data mining software SPSS Clementine to mine the factors that affect information from various vantage points and analyse that information. However the purpose of this paper is to review the selected software for data mining for improving efficiency of time series data. Data mining techniques is the exploration and analysis of data in order to discover useful information from huge databases. So it is used to analyse a large audit data efficiently for Improving the Efficiency of Time Series Data. SPSS- Clementine is object-oriented, extended module interface, which allows users to add their own algorithms and utilities to Clementine’s visual programming environment. The overall objective of this research is to develop high performance data mining algorithms and tools that will provide support required to analyse the massive data sets generated by various processes that is used for predicting time series data using SPSS- Clementine. The aim of this paper is to determine the feasibility and effectiveness of data mining techniques in time series data and produce solutions for this purpose.

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