14-11-2012, 11:51 AM
VISUAL DATA MINING
VISUAL DATA MINING.ppt (Size: 1.85 MB / Downloads: 46)
WHAT IS VISUAL DATA MINING?
In general , Data mining is the process of detecting patterns in a certain chunk of information.
Visual data mining is a new idea that uses recent technology to apply some specific principles to how humans interpret data.
It is a very general method, applied in detailed ways to get specific results, in areas like finance, medicine, public administration and government, transportation, and much more.
In visual data mining, programmers build interfaces that allow for visual presentations to be a part of how human users interpret the data that they see.
Computers can collect and display a huge amount of data. With data mining, humans can interpret this data in different ways.
That is the power that data mining brings to the human community, and the potential that its practitioners are looking at for improving modern methodologies.
WHY IT COMES INTO THE PICTURE?
Seeing is knowing, though merely seeing is not enough. When you understand what you see, seeing becomes believing.
Scientists discovered that seeing and understanding together enable humans to glean knowledge and deeper insight from large amounts of data.
The approach integrates the human mind’s exploration abilities with the enormous processing power of computers to form a powerful knowledge discovery environment that capitalizes on the best of both worlds.
Never before in history data has been generated at such high volumes as it is today.
Exploring and analyzing the vast volumes of data becomes increasingly difficult.
Information visualization and visual data mining can help to deal with the flood of information.
The advantage of visual data exploration is that the user is directly involved in the data mining process.
BENEFITS OF VISUAL DATA EXPLORATION.
For data mining to be effective, it is important to include the
human in the data exploration process.
Data Exploration is the process of searching and analyzing databases to find implicit but potentially useful information.
This combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and the computational power of today’s computers.
Hierarchical Techniques
Basic Idea: Visualization of the data using a
hierarchical partitioning into subspaces.
Dimensional Stacking:
Partitioning of the n-dimensional attribute space in 2-dimensional subspaces which are ‘stacked’ into each other.
Partitioning of the attribute value ranges into classes.
The more important attributes should be used on the outer levels and less important attributes should be used on inner levels.
THE FUTURE
All signs indicate that the field of visual data mining will continue to grow at an even faster pace in the future.
In universities and research labs, visual data mining will play a major role in physical and information sciences in the study of even larger and more complex scientific data sets.
It will also play an active role in nontechnical disciplines to establish knowledge domains to search for answers and truths.
CONCLUSION
This issue showcases an exciting field where people turn seeing into knowing, believing, and eventually human insights.
The active participation of humans and the decisions based on visualization combine the art of human intuition and the science of mathematical deduction, forever changing the landscape of data analysis.