International Journal of Swarm Intelligence and Evolutionary Computation

An Overview of Data Mining and its Applications

Nitines Kumar^* Department of Computer Engineering and Applications, GLA University, Mathura, India
^*Correspondence: Nitines Kumar, Department of Computer Engineering and Applications, GLA University, Mathura, India, Email:

Received: 06-Dec-2021 Published: 31-Dec-2021

Introduction

Data mining is a process where hidden data are analyzed according to multiple perspectives and turning those data into useful information and can be put into action. Data mining means collecting and assembling data from common areas, like data mining algorithms and data storages, look for patterns that can be used by businesses to ameliorate client service thereby increase their profit. It is known as knowledge discovery or data discovery. It’s veritably important in business intelligence to establish opinions that are driven by data.

Data mining is the process of assaying large volumes of data so as to discover business intelligence which helps companies to break problems, seize new openings, and mitigate risks. Some data mining tools used in the assiduity are Rapid Miner, mystic data mining, IBM SPSS Modeler, KNIME, Python Orange, Kaggle, Rattle, Weka, and Teradata. Data mining examples include Group on-Data mining allows Group on alignment of marketing conditioning nearly to client preferences which dissect just 1 terabyte of real- time client data that helps to identify the arising trends. Data mining fashion is used by Air France.

Trip searches, bookings, social media, flight operations, call centers, and relations in the field lounge are anatomized and a 360- degree client view is created. Grocery stores use data mining by giving fidelity cards to guests that make it easy for the cardholders to mileage of special prices that aren’t made available to noncardholders. The below are a many examples of data mining helping companies to increase effectiveness, streamline operations, cost reduction, and ameliorate gains.

The factual data mining task is the semi-automatic or automatic analysis of large amounts of data to prize preliminarily unknown,intriguing patterns similar as groups of data records ( cluster analysis), unusual records ( anomaly discovery), and dependences ( association rule mining, successional pattern mining). This generally involves using database ways similar as spatial indicators. These patterns can also be seen as a kind of summary of the input data, and may be used in further analysis or, for illustration, in machine literacy and predictive analytics. For illustration, the data mining step might identify multiple groups in the data, which can also be used to gain more accurate prediction results by a decision support system.

Data Mining Uses

Data mining is used in colorful fields like exploration, business, marketing, deals, product development, education, and healthcare. When used data mining provides an extreme advantage over competitive establishments by providing further information about guests and helps to develop better and effective strategies in marketing which will raise the profit and lower the cost. In order to achieve excellent results from data mining, a number of tools and ways are needed.

Advantages of Data Mining

The advantages of data mining are to enhances businesses to improve the time ahead by understanding the present and once and make precise predictions about the coming position, increase profit, understand the preferences and parts of guests, acquire new guests, ameliorate cross-sales and up- deals, increase fidelity and retain guests, increase ROI from marketing crusade, fraud discovery, credit threat identification and examiner functional performance.