Patterns in large sets of data are quite complex in nature and hence they are not easy to be discovered by traditional means. So, such information is discovered through the process of data mining. Patterns are analyzed in the form of mathematical analysis in case of data mining process.
The process of data mining can be used in a number of places such as:
- Forecasting like sales estimation,
- Probability and risking like customer selection for mailing,
- Recommendations like determining products for sale,
- Discovering sequences such as finding the next item that should be in the shopping list of a customer,
- Grouping of separating customers on basis of certain classification.
The process of mining has many extended process of asking questions, analyzing them and then answering them. The process altogether consists of six eminent steps and these are defining the problem, preparing data, exploring data, building models, exploring & validating models, and deploying & updating models.
The process of data mining is quite dynamic in nature as it can be known that only a particular set of data is not sufficient in creating proper mining models. More data is required for which more models are created. But at a certain point, it is realized that these models together also do not solve out the problem correctly. Again these models may have to be updated again and again as new data may have been added, in order to get a perfect model.
Microsoft SQL Server Data Mining offers a perfect SQL Server Development Studio that comprises of algorithms and tools for data mining and also for browsing models.
Defining The Exact Problem
As per the model, the first important thing to be done is to define the problem for which a solution has to be achieved. There are a number of steps to solve the problem such as analysis of the business requirements, defining the evaluation of the model to be used, defining data mining objectives and others.
Some of the important questions to be asked can be about the distribution of the data, effect on the process of the business, kind of data to be used and others. The questions can be also answered by conducting an investigative study regarding the availability of data.
Preparing The Data
The data that has been found in the above step has to be cleaned and has to be prepared for further processing. There can be a number of data that may be of no use or may be available in a hidden way with some missing values. It is important to fix these data for further use.
Exploring The Data
After the data has been prepared, now the third step of analyzing and exploring the data is used. Some of the techniques such as calculating standard deviations, and mean values are used for exploring the data. It is done to understand the problem of the company in a better way and for this a number of tools can be used such as Data Profiler Integration Services.
Model Building
Creating models is all about creating structures with various columns. These are mainly different algorithms for which various parameters can be used. A new model can be defined by using various SQL Server Data tools.
Exploring Models And Validating Them
Even when the model is ready to be used in practical environment, it is important to explore for final testing. There are tools from the Analysis Services that offers separation of datasets from data for analyzing the performance of the model.
Update of the Models
The validated models can be used in a number of ways for various processed such as creating predictions, creating formula, processing mining structures and creating a report. But it is important to update the models from time to time in order to get more features and use the models dynamically.
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