Prevalence of Visualization Techniques in Data Mining

2019 ◽  
pp. 273-298
Author(s):  
Rosy Madaan ◽  
Komal Kumar Bhatia
Keyword(s):  
Data Mining ◽  
Visualization Techniques

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING

2001 ◽  
Vol 10 (04) ◽  
pp. 691-713 ◽  
Author(s):  
TUBAO HO ◽  
TRONGDUNG NGUYEN ◽  
DUCDUNG NGUYEN ◽  
SAORI KAWASAKI
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Knowledge Discovery ◽  
Performance Metrics ◽  
Model Performance ◽  
Discovery Process ◽  
Whole Process ◽  
Discovery System ◽  
Competing Models ◽  
Visualization Techniques

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

scholarly journals Using data mining and visualization techniques for the reconstruction of ocean paleodynamics

2003 ◽  
Author(s):  
R. Theron ◽  
J.A. Flores ◽  
F.J. Sierro ◽  
C. Pelejero ◽  
J. Grimalt ◽  
...  
Keyword(s):  
Data Mining ◽  
Using Data ◽  
Visualization Techniques

scholarly journals Visualization Techniques for Data Mining

2008 ◽  
pp. 1623-1630
Author(s):  
Herna L. Viktor ◽  
Eric Paquet
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Large Data ◽  
Analysis Data ◽  
Rule Induction ◽  
Induction Programs ◽  
Data Repositories ◽  
Use Of The Internet ◽  
Visualization Techniques ◽  
Mining Tools

The current explosion of data and information, mainly caused by data warehousing technologies as well as the extensive use of the Internet and its related technologies, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, is aimed at addressing this need. Data mining automates the discovery of hidden patterns and relationships that may not always be obvious. Data mining tools include classification techniques (such as decision trees, rule induction programs and neural networks) (Han & Kamber, 2001), clustering algorithms and association rule approaches, amongst others.

Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

2009 ◽  
pp. 200-223
Author(s):  
Sadok Ben Yahia ◽  
Olivier Couturier ◽  
Tarek Hamrouni ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Efficient Solution ◽  
Explicit Knowledge ◽  
Interactive Visualization ◽  
Information Loss ◽  
Compact Representation ◽  
Knowledge Based ◽  
Extraction Step ◽  
Visualization Techniques

Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules. These tools must be able to generate explicit knowledge and, then, to present it in an elegant way. Visualization techniques have shown to be an efficient solution to achieve such a goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction step. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both data mining steps (association rule extraction and visualization) are treated separately in a one way process. Recently different approaches have been proposed that use meta-knowledge to guide the user during the mining process. Standing at the crossroads of Data Mining and Human-Computer Interaction, those approaches present an integrated framework covering both steps of the data mining process. This chapter describes and discusses such approaches. Two approaches are described in details: the first one builds a roadmap of compact representation of association rules from which the user can explore generic bases of association rules and derive, if desired, redundant ones without information loss. The second approach clusters the set of association rules or its generic bases, and uses a fisheye view technique to help the user during the mining of association rules. Generic bases with their links or the associated clusters constitute the meta-knowledge used to guide the interactive and cooperative visualization of association rules.

On visualization techniques for solar data mining

2015 ◽  
Vol 10 ◽  
pp. 32-42 ◽  
Author(s):  
M.A. Schuh ◽  
J.M. Banda ◽  
T. Wylie ◽  
P. McInerney ◽  
K. Ganesan Pillai ◽  
...  
Keyword(s):  
Data Mining ◽  
Visualization Techniques
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