A database system, at its core, reduced to its basic components, consists of data, hardware and software. Computer hard disks and memories show increased capacity and reduced cost each year. Since the cost of data storage keeps on dropping, users store all the information they need in databases. This write up may give some insight about knowledge discovery in databases and data mining, the usage and the very purpose of KDD and some methods of discovering the knowledge from databases. I manifestly. The size of the database, whether scientific or business, did grow at a
fast rate. Reasons for this growth can be found in the technical advances that allow
a system to acquire a higher amount of information as well as in the each time more
common use of databases.
We have reached a point where the amount of information
stored in database exceeds by far the analysis capabilities that the methods used up
to now have to offer. That is why there is a growing need for a new generation of tools
that can help the data analyst. The problem these tools have to solve is the search for
small pieces of knowledge in huge amounts of data. Such tools are the object of study
of the field named Knowledge Discovery in Database.
Decision-making is an essential process in most of the business fields. Quick but sound
decisions in many corporations are important to achieve competitive advantages.
Nevertheless, these are time-consuming and labor-intensive tasks due to the overwhelming
amount of data that are required to be processed and understood to make the necessary
decisions. Thus, an automated tool for analyzing and mining large amount of data in order
to provide quick and correct decisions is greatly needed.
The need to make decisions
involves careful study on the organization-wide information. Throughout the years, many
organizations strive to automate their decision-making processes by implementing
analytical tools in order to bypass tedious information processing tasks.
Until recently, IT departments with big budget projects involving data have focused their
attention on data ware housing activities: Collecting data in different formats from disparate
sources and consolidating that data into a central repository. During these multiyear,
massive data warehousing projects, few gave much thought to how this data could be
"mined" to discover patterns and unexpected relationships. KDD is relatively new discipline |
