When there is lot of data available, there is a
confusion about which data is useful and which data is not. Data
mining helps to find important and actionable data from the big data.
When the data is mathematically certain patterns and trends can be
found. These patterns cannot be found out by using old techniques.
One reason is that there is lot of data. The other being the very
complex relationships among the data.
The data mining model is a collection and definition
of these data mining models. There are many mining models and each
caters to a specific scenario.
- Recommendations: An example of recommendation is deducting which two products will sell together in a retail shop.
- Finding sequences: Suppose a person is shopping online, the next purchase can be guessed by studying the existing items in the cart.
- Grouping: Based on similarities in the data customers and/or events can be grouped together.
- Forecasting: Few example include sales estimates, server loads study or server downtime data.
- Risk and probability: Can decide which customer will receive our targeted e-mails, Diagnose by applying probabilities.
PLC
Technologies has helped students in implementing IEEE projects in
data mining. If you are a final year student looking for the right
place to execute your project - contact PLC Technologies. For more
info visit Data Mining IEEE Mini Final Year Projects 2013/2014 or
call 044-42005050/60.
