LEARNING DIARY ON DATA MINING:

LEARNING DIARY ON DATA MINING.

Am very interested on the topic of Data Mining which was presented by Mr Joshua Sendu and Remy Kaaro on 17/04/2009 the topic was good and well understood.
On top of that have learned a lot of things on Data Mining and comes to realized that is good and well presentation from my colleges.

THE MEARNING OF DATA MINING.

Data mining. Is the process of extracting hidden patterns from large amount of data.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

BACKGROUND.

Humans have been "manually" extracting information from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches in order to simplify the collection of information from data by doing so their increasing power of computer technology has aided for easier data collection, processing, management and storage. However, the captured data needs to be converted into information and knowledge to become useful easier for collection the information. Data mining is the process of using computing power to apply methodologies, including new techniques for knowledge discovery, to data.

There are three stages of data mining, which are;

Data Exploration This stage usually starts with data preparations which involve cleaning data and data transformation. Data exploration is a methodology in which manual techniques are utilized to find one's way through a data set and bring important aspects of that data into focus for further analysis. Though such a methodology can be applied to data sets of any size or type, its manual nature makes it more reasonable for smaller data sets, especially those in which the data has been carefully gathered and constructed.

Model building and validation:

This stage involves considering various models and choosing the best one based on their predictive performance can be good for use.

Deployment: This is the final stage which involves 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

There are several advantages of the data mining then he following are some of them, banking here the bank can find the good information and then they can analyses for a good manner then advertise to the people then they can learn very well, another advantages like Marking, for the researchers, Law enforcement.
In other hand regardless there are some advantages, data mining also have some disadvantages then the following are as follows security in case of this it need a very highly security to protect the data if the technology used to store may be to collect the information is not good or properly it leads to lost the information quickly,Misuse of information; if the information not used properly it can be easier for other people to know the secret information that not need to be known by the other people, Privacy issues; there are some issues which people do not want them to be known as public issues that is why are kept in hidden.

Conclusion: Data mining is a good topic and people should understand well for there benefit for example to hide the information not each to be public ally. Therefore the topic was good.