DATA MINING

LEARNING DIARY ON DATA MINING.
Am very interested with the presentation topic of Data Mining which was accessible by
Mr. Joshua Shendu &
Remy Kaaro

On 17/04/2009 there was the occurrence of the presented topic which was the best and well comprehended. This topic is less smooth but I came to appreciate that is excellent and well due to the fact that the presenters were prepared to do their task!
THE MEARNING OF DATA MINING. Data mining.
- Data Mining (also known as Knowledge Discovery) technology helps businesses discover hidden data patterns and provides predictive information which can be applied to benefit the business
Or
-As the process of investigate data from different point of view and shortening it into useful information that can be used to increase income, cuts expenses, or both. Or is the process of finding correlations or patterns among dozens of fields in large relational databases.

Data mining software is analytical tool used for analyzing data and consent to users to examine data from a lot of unusual magnitude or angles, classification, and sum up the associations recognized.

BACKGROUND.
Data Mining and Knowledge Discovery in Databases are terms used interchangeably. Other terms often used are data or information harvesting, data archeology, functional dependency analysis, knowledge extraction and data pattern analysis. A high level definition of Data Mining is: the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Data mining is not a simple process and there is no tool that can do the job automatically. Data mining can be aided by tools, but it requires both human data mining expertise and human domain expertise. Data mining consists of a number of operations, each of which are supported by a variety of technologies, such as rule induction, neural networks, conceptual clustering. In real world applications information extraction requires the cooperative use of several data mining operations and techniques

STAGES OF DATA MINING
There are three stages of data mining, which are;
(i)Data Exploration
-This stage usually initiates with data arrangements which involve cleaning of data and data transformation into useful ways. In this stage a methodology way in which techniques are manual consumed to find one's way through a data set and bring about imperative aspects of that data into focal point for further investigation.
Though such line of attack can be applied to data sets of any size or types when its manual nature makes it more reasonable for less important data set with especially those in which the data has been carefully gathered and constructed as well.
(ii)Model building and validation: This stage absorb in considering various models and prefer the best one based on their predictive performance can be high-quality for use.
(iii)Deployment:
This is the final stage which involves in using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcomes

ADVANTAGES OF DATA MINING
The key reason why Data Mining is such a slogan at the moment is that because many organizations recognized the need to better understand their customers. Data mining can deliver real world results. Data mining has been used for the following types of applications:
(i)Understanding purchasing behaviors of customers
(ii)Detecting credit card or insurance fraud
(iii)Predicting probable changes in financial markets

PROBLEMS OF DATA MINING
One of the problems with data mining software has been the rush of companies to jump on the band wagon as these companies have slapped `data warehouse' labels on traditional transaction-processing products, and co-opted the dictionary of the industry in order to be considered players in this fast-growing category.
Many company Systems have established a criterion for a relational database management system (RDBMS) suitable for data warehousing, and documented specialized requirements for an RDBMS to qualify as a relational data warehouse server. According to most of company system, the requirements for data warehouse RDBMSs begin with the loading and preparation of data for query and analysis. If a product fails to meet the criteria at this stage, the rest of the system will be inaccurate, unreliable and unavailable.
Also Data mining systems rely on databases to supply the raw data for input and this raises problems in that databases tend be dynamic, incomplete, noisy, and large. Other problems arise as a result of the adequacy and relevance of the information stored.

Conclusion
Data mining is typically not used as a business system delivery technology.
Rather it is an extremely powerful and effective set of technologies for analyzing and clustering data which can be used to form the basis of a system
By Venance.