The main aim of this paper is to expound about the
knowledge discovery and data mining.Data warehouse brings the
information from multiple sources so as to provide a consistent
database source for decision support queries and off-load
decision support applications from the on-line transaction
system. Here, data is available but not information and not the
right information at the right time. Data mining is extracting
interest information or patterns from data in large databases.For
processing the data there are many traditional and statistical
methods of data analyses and spreadsheets are used to obtained
informative reports from data but they can’t give the knowledge
from data. Closet is an efficient algorithm which is scalable on
large databases in order to get the important knowledge hidden
inside the data. In this process a set of association rules are
discovered at multiple levels of abstraction from the relevant sets
of data in a database.
Published In : IJCSN Journal Volume 3, Issue 6
Date of Publication : December 2014
Pages : 532 - 535
Figures : 01
Tables : 04
Publication Link : Knowledge Discovery in Database
KIRAN S. GAIKWAD : Working as IT Lecturer,
Government Polytechnic College.
IT Experience: 4years.
B.Tech (Computer Science)
Data mining is an iterative process of extracting interesting
knowledge from data in large databases. Where knowledge
could be rules, patterns, regularities, relationships,
constraints etc. Whereas KDD is the overall process of
finding and interpreting knowledge from data it helps the
workers in their everyday business activity and improve
their productivity and also helps for knowledge workers
like executives, managers, analysts to make faster and
better decisions .So knowledge should be valid and
potentially useful and then the hidden information in the
database will be useful.
[1] Anahory S, & Dennis M, “Data Warehousing in the Real
World” Kimball R, “Data Warehouse Toolkit”,John Wiley.
