DATA MINING AND VISUAL ANALYTICS RESEARCH GROUP Introduction In recent years, the data mining research community has seen a drift towards the utilization of visual representation of information for analytical reasoning, knowledge extraction and decision making. This drift has given birth to a new research domain called Visual Analytics. People also use the terms such as Information Visualization and Visual Data Mining to refer to this new and exciting field. The classical visualization pipeline for visual analytics is shown in Figure 1. From Raw Data to producing a visual representation for a user to interact and acquire knowledge, there are several steps as shown in the figure. Research in all these areas is actively persued by scientists around the world. Figure 1: Visualization Pipeline for Visual Analytics
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DATA MINING AND VISUAL ANALYTICS
RESEARCH GROUP
Introduction
In recent years, the data mining research community has seen a drift towards the utilization of
visual representation of information for analytical reasoning, knowledge extraction and decision
making. This drift has given birth to a new research domain called Visual Analytics. People also
use the terms such as Information Visualization and Visual Data Mining to refer to this new and
exciting field.
The classical visualization pipeline for visual analytics is shown in Figure 1. From Raw Data to
producing a visual representation for a user to interact and acquire knowledge, there are several
steps as shown in the figure. Research in all these areas is actively persued by scientists around
the world.
Figure 1: Visualization Pipeline for Visual Analytics
The goal of visual analytics is to facilitate the users to interactively search for information,
deduce important facts and identify interesting patterns which in turn, can be used by domain
experts for decision making. Visualization supports this entire process by involving users to
exploit the human capacity to perceive, abstract and understand complex data and information
available to the user.
The use of visualization is fast becoming a crucial analysis technique in a number of different
areas. These areas include but certainly are not limited to:
• Economics: Stock Market Patterns and Analysis.
• Sociology: Social Network Analysis.
• Technology: Exploration of Information on the Web.
• Tranportation: Optimization of Air, Road and Sea travel across the globe.
• Geography: Migration behavior for cities, countries and continents.
• BioInformatics: Analysis and Mining of Biological Networks.
Figure 2 represents visual layouts of data from three of the above mentioned fields. A recent U.S.
report to the funding agencies NIH and NSF provides strong arguments in favor of the
development of visualization as a research field:
“Visualization is indispensable to the solution of complex problems in every sector, from
traditional medical, science and engineering domains to such key areas as financial markets,
national security, and public health. Advances in visualization enable researchers to analyze and
understand unprecedented amounts of experimental, simulated, and observational data and
through this understanding to address problems previously deemed intractable or beyond
imagination.”
[from the Executive summary of (Johnson, Moorhead et al. 2006)]
Figure 2: Molecular Structure, Social Network of Hollywood Actors and
Metabolic Pathways
Aims and Objectives
The Data Mining and Visual Analytics (DaMiVA) research group aims to develop algorithms,
models and systems for technological advancements in the area of Data Mining and Visual
Analytics.
Our goal is to focus on large size relational data and develop high speed and efficient algorithms
for extraction of knowledge, discovering hidden patterns and support interactive data mining
through user interactions.
Often relational data can be represnted through graphs and networks. The term ‘network’ has
different significations for people from different walks of life. The term is used extensively to
represent systems such as social networks, electrical circuits, economic networks, chemical