Data mining is the process of discovering the large values of information from the large sets of data. It is the process used by large companies which contains large sets of data, which turn the raw data into useful information as per end user requirements. This can be done by software which is used for the current analysis and future analysis of data. These data mining programs analyze the relationships and patterns for the user specified request. This software is used by big companies so as to know their customers and develop more effective strategies in order to develop their company. It mainly depended on the effective data collection and data warehousing as well as computer processing. This technique cannot be used if we are dealing with traditional data.

Learn more about this technology MSBI Online Training in Bangalore in this overview

Data mining process:

This data mining process is done in five steps:

  • It collects the data and stores the data warehouses.
  • They can store and manage the data either in data warehouses (or) cloud
  • Business analyst collects the data from those based on the requirement and determines how they want to organize it.
  • This data mining tool sorts the data based on the user results.
  • The end user finally presents the data in an easily–sharable format

The usage of this software is explained with the example given below:

Suppose a Restaurant want to know about their business over the particular time period , they simply the program to know the details of which items is sold more  on which time  and which don’t  based upon the customer visit over the previous period ,so as to know  the quantity supposed to  be required in the coming month and prevent the loss of unnecessary  products / good/ food items.

The scope of Data mining:

The data mining has a very good scope and has bright futures.It helps to identify the analysis of previous data and also gives the predicated analysis of future data. Its scope can be explained as follows:

Automated prediction of tools and behaviors:

It automates the process of finding the predictive information in large databases. By using the data in the past, it helps to identify the analysis of the future in the maximize the Return on Investment (ROI).

Automated discovery of previously unknown patterns:

Data mining tools sweep through databases and identify the hidden patterns in one step. It helps to know the previous data results in a retail industry even though the products were dissimilar

 Data  Mining process:

The process of data mining can be done in the following manners as shown below.

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Defining the problem:

It is the first step in the data mining process. This phase is responsible for understanding the problem. Some of the examples were listed below :

    What is the input data given to you?

    What do you need to retrieve?

Preparing data:

Based on the data given to you at the time of defining the problem, we need to prepare the data for further analysis.

Exploring the data: 

From the data given to you, we need to some explorations like calculating the maximum, minimum values, calculating mean

Building data models:

 You can create a mining structure by creating and defining columns. This mining is not linked to any other source of data until you process the data.

Validating the data models:

The built model must be validated in order to get into the life for processing all types of data.

Deploying and updating the models:

The model must be updated, If there are any updates required by the validation model and the must be deployed for the clients to use.

Data mining techniques:

Data mining can be done by many techniques.Some of them were listed below:

Nearest Neighbourhood methods:

It is a technique that classifies each record in the database set to the combination of K-records to its historical data set

Decision trees:

Whenever a program (or) algorithm is executing it would not go always linearly.Sometimes it may go through several deviations.While going through algorithm (or) program, we need to several deviations

Genetic algorithms:

optimization techniques that use processes such as genetic combination. Mutation etc

Rule Induction:

it is used when the data is in statistical form.

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It’s better to have a fundamental knowledge of keeping up and examining them for implementation. No Programming abilities were required to take in this course. It’s great to have knowledge of the utilization of Excel or the worksheets to understand the whole design of MSBI.

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