Data Mining is a part of a larger process of knowledge discovery and it is the process by which data is automatically extracted in order to provide useful information that is predictive in nature and which may not have been known previously.
2. There are three steps in…
The second stage is Predictive Modeling, whereby patterns discovered in the earlier stage are used to make predictions about the future. The third stage is Forensic Analysis, where the patterns extracted are used to find unusual data elements.
3. One of the pitfalls of data mining is the vast quantities of data that are generated(Khabaza, 2005). When the volume of data is too high, mining becomes sluggish, hence the way to avoid this is by using sampling. Another is the generation of irrelevant data, so that the amount of relevant data mined may be less. Thirdly, if data mining is disorganized, it takes place in an ad hoc manner and will not generate useful results. Avoiding this requires clear definition of goals. When there is incompatibility in data mining tools, this causes interference in exploratory capability and high overhead costs.
4. The data mining program was used to identify hidden trends in the data. The airline company can use the data to identify the specific characteristics of those customers who are frequent users of the airline. The mined data can also be used to find a relationship among different sectors based upon customer behavior.
5. Two specific industries where data mining is likely to be very useful are banking and the retail industry. With the increase in electronic banking, transactional data can be easily captured and data mining helps analyze it. Data mining in the banking industry can help banks to analyze trends and patterns and to predict how customers may react to change sin interest rates. In the retail industry, data is collected when orders are placed and data mining of such information can unearth demographic trends in the data and can help direct marketing efforts.
* Khabaza, Tom, 2005. “Hard hats for data miners: Myths and pitfalls of Data Mining”, DM Review ...
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