20-08-2014, 03:54 PM
INTELLIGENT DATA MINING DATA MINING POWERED BY ARTIFICIAL INTELLIGENCE PROJECT REPORT
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Abstract
Data mining is proving to be a great tool for
exploring new avenues to automatically examine,
visualize, and uncover patterns in data that
facilitate the decision-making process. “The
system can interpret details that escape the
researcher, especially in cases where product
vendors would like to know their customer’s
buying trends,” says the analyst. It simplifies the
task of inferring information and patterns from
data that might run into hundreds of pages. AI
based algorithms impart a ‘sixth sense’ to the data
mining systems. AI algorithm powered Data
mining opens up new ways to explore data.
With the Internet becoming the main support
system of most organizations, data size and
contents has multiplied. Richer multimedia
contents and conversion of paper sources to
electronic records have done little to ease the boom
in unstructured data. To top it all, the continuous
exchange of data between organizations – in
structured forms of phone calls and file
attachments – has built up large volumes of data in
types, formats,and languages that are not entirely
usable. Artificial Intelligence (AI) can help to
convert all these data into structured, usable
formats. Data mining or knowledge discovery is
becoming more important as more and more
corporate data is being computerized. Intelligent
applications, such as neural networks and genetic
algorithms are ideal for finding trends and
unknown information from the vast quantities of
computer data.
Introduction
In 1997, after he was defeated by IBM’s Deep
Blue supercomputer, the flesh and bones world
chess champion Garry Kasparov was quoted as
saying the computer played “Like a Go”. That
chess match and all its implications raised
profound questions about Artificial Intelligence.
Many saw it as evidence that true Artificial
Intelligence has finally been achieved. After all, a
man was beaten by a computer in a game of wits.
But it’s one thing to program a computer to solve
the kind of complex mathematical problems found
in chess. It’s quite another for a computer to make
logical deductions and decisions.
The concept of Artificial Intelligence, or AI, has
been around since the 1970s. Originally, the
primary goal was for computers to make decision
without any input from humans. But it never
caught on, partly because system, administrators
couldn’t figure out how to make use of all the data.
And even if some could conceive the value of the
data, the stuff was very hard to use, even for
engineers On top of that, the challenge of extracting data
from the rudimentary databases of three decades
ago was significant. Early AI implementations
would spit out streams of data, most of which was
not sharable or adaptive to different business
needs. Instead, the inability offends users to deal
with its complexity and expensiveness and their
lack of understanding of its potential caused these
expectations to dwindle.
These factors slowed down the adoption rates of
AI, but not the efforts of researchers. After a
couple of decades, AI, now in the form of
applications, is slowly making its way out of
laboratories into the mainstream market.
Current Scenario
Currently, all web searching does is find relevant
documents from which you might be able to
extract your answer, but sometimes it doesn’t do
that because it doesn’t understand what you are
asking. We are talking about simple reasoning with
a bit of understanding behind it. Machines may one
day overtake human intelligence levels because,
with the ability to accelerate their own education,
there is no limit to how smart they could become.
The question is whether AI has progressed to the
point where humans can trust computers to
evaluate a situation and make accurate
Conclusion
“In future, AI is likely to be incorporated in several
products to make user’s live easier”, commented
by Technical Insights Research Analyst Amreetha
Vijay Kumar. However, it cannot be depended on
to replace human intelligence and can serve only
as an enabling technology. In the not too long
term, data mining may become as common and
easy to use as E-mail. In many scientific
applications, where data is distributed and large,
the concept of utility in AI is used to evaluate thecost of data mining tasks (e.g., data acquisition,
data mining, and model utilization) so that
knowledge discovery is practically feasible in
resource constrained environments. AI algorithm
powered Data mining opens up new ways to
explore data