To technically define the process of ‘data mining’, one could say that it is an automated extraction of information for their predictive analysis. This information is hidden into the overwhelming amounts of databases.

To put it in simple words, retrieval of data that is deemed to be important from the large amounts of datasets or data. This data is then presented in an analyzed form for the purpose of making decisions for the business.

The process of data mining requires putting into use the various types of mathematic algorithms as well as statistical techniques thrown in together along with software tools.

The use of BI Data mining is implemented for the purpose of market research, competitor analysis and for industry research.

What Are The Steps Involved In Data Mining?

Storage of Data: There is an enormous amount of data available around us, and more data is being generated every second. There is a need for storage of this data, and the pre-processing steps are quite essential for the success of its analysis.

Selection of responses: Selection of the response variable data hk  that are appropriate should be done and one should decide the figure of variables that should be examined.

Screening of the data: For outliers, there is a need for screening the data. Other missing values have to be addressed, these include values that are omitted or those appropriately imputed by one of the many methods available.

Determination and Analysis of the Data: There is a need for the data sets to be divided into evaluation and training data sets. In the case of data sets that are very large, they can’t be interpreted and analyzed so easily, therefore for doing so, the data should be sampled.

Visualization of the Data: Before the application of sophisticated models, the data needs to be summarized as well as visualized. By the use of basic graphs inclusive of line graphs and bar charts, scatter plots, plus matrix plots, histograms and box plots, one can use them for time series, categorizing the variables, display the correlation matrices, and multidimensional graphs with color, to overlay plots, visualization of the network data, Geo maps as well as spatial data, etc. All of these are used for the purpose of graphical displays.

For the construction of good graphs, there need to be accuracy about the correct labeling, and scaling along with aggregation and issues pertaining to stratification.

Summarizing the data: For the summarization of the data, a few of the typical summary statistics are involved such as standard deviation, correlation, percentiles, and median, etc. They are considered amongst one of the more advanced summaries like principal components.

Business Intelligence is considered to be a broader area for the making of decisions involving the use of data mining as a tool. With the help of Data mining, the data in business intelligence becomes more relevant for usage. There exist, various kinds of data mining. They are inclusive of social network data mining, pictorial mining, web mining, relational databases, text mining, web mining, video data mining, etc. All of these are implemented in the field of Business Intelligence.


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