How data mining contributes to big data analytics

How data mining contributes to big data analytics

Data mining and big data are completely different concepts, but somehow they are related to each other.

Big data reflect a company’s hitch of large amount of data difficult to store and handle. This propels some obstructions with accurately analyzing this information. Big data is basically overgrown data difficult to be stored under conventional databases and data handling structures. Such data is difficult to be stored in software like Microsoft Excel. The situation is rising at very fast pace with the growing companies. Some upcoming companies in India are facing the same trouble. To ease up this trouble data mining is becoming rising solution. There are many companies that provide data mining services in Indiaand proved to be really helpful.

Data mining is the process that starts with managing such an extensive data and goes on till the complete analysis to bring out the usefulness out of this information. Scrutinized information is further used in strategic planning. The process is based on applying some algorithms to find out the relation among the variables thereby pulling out some profitable trends. Four data mining techniques are largely used to examine such sizable databases.

data mining

  1. Association:

Though the purpose of all data mining techniques is to develop link among the two or more variables, association basically projects how one variable affects the other one. For example how age of a purchaser group can affect the sale of a product.

  1. Clustering:

Cluster detection basically examines data with a view to recognize sub-categories within a database. The motive is to combine related information. The risk of missing out the important categories falls down to bare minimum.

  1. Classification:

After combining the relative information and forming identifiable categories, there are chances that some new or exceptional cases are missed out. Classification technique reduces these chances missing out such cases, which we might fail to recognize manually.

  1. Regression:

Regression technique is followed to construct predictive models. Production firms use predictive analysis to explore the consumer behavior and then decide the production level, as production and sales varies with customer demand.

Data mining has been extremely supportive in deriving some meaning out of these sizable databases. Big data was a growing concern for almost every organization. With emergence and popularity of data mining the concern has been eased out.