What is Data Mining?

What is Data Mining?

Data Mining is the process of extracting information from large sets of data. Data Mining is called Knowledge Discovery in Data (KDD). It pulls valuable datasets that help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Usually, Data Mining is done by data scientists and other professionals.

For mining comprehensive data sets, machine learning algorithms and artificial intelligence automate the process more efficiently. Several Organizations gains benefit by using data mining for filtering the required data, taking quick business decisions, improve the business ideas and strategies etc.

Steps involved in Data Mining

The various steps involved in data mining are,

• Data Gathering

Relevant data are gathered in structured and unstructured data stored in a data warehouse or data lake. Data Scientists manage the data wherever it comes from the resources.

• Data Preparation

This process involves the data being mined using data exploration, pre-processing and profiling and data cleansing is done to fix the errors and quality of the data. Finally, data is filtered for next step process.

• Data Mining

If the data is set for the mining process, the data scientists choose appropriate mining techniques and algorithms for the mining process. The algorithms are trained in such a way as to look at the information being sought before they run against the complete data set.

• Data analysis and Interpretation

Results generated by data mining are used to create analytical models that help drive decision-making and various business actions. The data scientists and another data science team member communicate with the business executives and users through data visualization and data storytelling techniques.

Techniques Involved in Data Mining

The techniques involved in Data Mining are,

Association rule Mining- In the data mining process, an if-then statement is used to identify the relationship between data. It determines the number of times the datasets are repeated to check the if-then information is accurate.

Classification-It differentiates the elements in the data set based on their categories. Various methods are used to categorizecategorize the aspects, such as Decision trees, Naïve Bayes classifiers, k-nearest neighbours and logistic regressions, which are used to predict classification.

Clustering-Clustering is the process of grouping the characteristics of data elements which is a part of data mining applications. Some examples include K-means clustering, hierarchical clustering and Gaussian mixture models.

Regression-It calculates the predicted data values based on the set of variables. Some of the examples include linear regression, decision trees and multivariate regression are used.

Sequence and Path Analysis- Data is mined to identify patterns for a particular set of events of values which leads to later ones.

Neural Networks- As the name suggests it works based on human brain activity. It is specially used in complex pattern recognition, which involves deep learning and advanced offshoot machine learning.

Applications of Data Mining

The various applications of data mining are,

Retail-Online retailers mine customer data and Internet click stream that records and helps to target marketing campaigns, including ads and promotional offers to individual shoppers. Data mining suggests the personal purchase of the possible things based on website visitors, including inventory and supply chain management.

Financial Fields-Companies such as Bank and credit card use data mining tools to build economic risk models and detects fraudulent transactions, credit applications and vet loans

Insurance- In the Insurance field, it uses data mining tools for pricing insurance policies and approving policy applications which include risk modeling and management for the customers

Entertainment-In entertainment field, it helps the companies to let them know what people’s choice is and track their views. With the help of this the company launch product based on users choice.

Healthcare-It helps to check medical conditions, analyze X-rays, treat patients and results of medical imaging. Medical research is also done using data mining and machine learning.

Pros of Data Mining

The benefits of data mining include,

• Increased Marketing and Sales
• Increase in Production
• Low Cost
• Stronger Risk Management
• Increase the Production uptime
• Increase in supply chain Management
• Good Customer Service

Summary

From the above article, the uses of data mining are discussed here. Nowadays, large set of information is processed daily; hence, data mining is a familiar concept with a good career scope in the future.