Knowledge Discovery in Databases: An Overview
Abstract
In the past, the term Data Mining was, and still is, used to designate the activity of pulling useful information from databases. Now, this term is recognized to apply but to one activity in a very large process to extract knowledge from opaque databases. The overall process is known as Knowledge Discovery in Databases, (KDD). This process is comprised of many subprocesses which when linked together provide a firm foundation for knowledge acquisition from large databases. Many tools, techniques, and disciplines come together under the umbrella of KDD.
Introduction
Today, the topic of data mining has much interest in government, business, and research circles. With the growth of computer use within these areas has also come a greater desire to let the computers do the work that used to be done by humans. The problem, nowadays, is that the data that needs to be analyzed has become too large and cumbersome for one person or even teams of people to envision tackling without help from computers. These computers are no longer mere crunchers of numbers but now they find the patterns that the humans used to find. From this growth has arisen a vast body of knowledge concerned with this process of data analysis. As with much other information, the Internet is employed to make available the ever-growing body of information on this topic. Many general sources of information [a,b,c] are now online. These are updated and expanded upon almost a constant basis. The use of the Internet to disseminate and collect information is itself a consideration in this field. The amount of information is expanding at such a rate that old methods of information disposal, such as paper journals and b...
... middle of paper ...
...11) R. Lippman, "An Introduction to Computing with Neural Networks", IEEE ASSP Magazine: 4:2 (1987), pp.4-22.
12) C. Murphy, G. Koehler & H. Fogler, "Artifical Stupidity", The Journal of Portfolio Management: 23:2 (Winter 1997) pp.24-29.
13) J. Quinlan, "Induction of Decision Trees", Machine Learning: 1:1 (1986), pp.81-106.
Hyperlinks
a) http://www.cs.bham.ac.uk/~anp/TheDataMine.html
b) http://www.gmd.de/ml-archive
c) http://info.gte.com/~kdd/
d) http://info.gte.com/~kdd/corporate.html
e) http://info.gte.com/~kdd/datasets.html
f) http://info.gte.com/~kdd/siftware.html
g) http://www.almaden.ibm.com/stss/
h) http://www.research.microsoft.com/research/datamine/
i) http://www-aig.jpl.nasa.gov/kdd95/
j) http://www-aig.jpl.nasa.gov/kdd96/
k) http://www.neuronet.ph.kcl.ac.uk/
l) http://www.ics.uci.edu/AI/ML/Machine-Learning.html
Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users.
Kandel, E. R., J. H. Schwarz, and T. M. Jessel. Principles of Neural Science. 3rd ed. Elsevier. New York: 1991.
Johnson, G., Scholes, K., Johnson, G. and Whittington, R. 2011. Exploring strategy. Harlow: Financial Times Prentice Hall.
Barbara Mowat and Paul Warstine. New York: Washington Press, 1992. Slethaug, Gordon. A. See "Lecture Notes" for ENGL1007.
Stergiou, C., & Siganos, D. (2011, August 6). Neural Networks. Retrieved August 6, 2011, from
Ross, S.A., Westerfield, R.W., Jaffe, J. and Jordan, B.D., 2008. Modern Financial Management: International Student Edition. 8th Edition. New York: McGraw-Hill Companies.
Investor psychology and security market under- and overreactions, Journal of Finance, 53, No. 1. 6, pp. 58-78. 1839 - 1885 - 1885. i.e. a. Burton G. Malkiel, 2003. The Efficient Market Hypothesis and Its Critics, Journal of Economic Perspectives, Vol.
William Sharpe, Gordon J. Alexander, Jeffrey W Bailey. Investments. Prentice Hall; 6 edition, October 20, 1998
Morgenson, G. (2005, September 17). Clues to a Hedge Fund's Collapse. In The New York Times. Retrieved November 1, 2013
Nathanson, M. (1984) Using Artificial Intelligence Systems May be Smartest Way to Trim Costs, Modern Healthcare, Volume 14. Page 138
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There is a debate between the benefits and potential informational privacy issues in web-data mining. There are large amount of valuable data on the web, and those data can be retrieved easily by using search engine. When web-data mining techniques are applied on these data, we can get a large number of benefits. Web-data mining techniques are appealing to business companies for several reasons [1]. For example, if a company wants to expand its bu...
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
6- Developing of new tools, for all the new fields of studying, and developing programs for data mining and analysis of huge databases.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.