Volume 16, No 2, 2019
Data Mining: A Process Of Extracting Patterns
Abhay Bhatia , Parag Jain , Praveen Verma , Gaurav Gupta , Deepak Arya , Barkha Chaudhary
Abstract
The technique of extracting patterns from data is known as data mining. In a nutshell, data mining is the process of analysing observational datasets to discover unexpected relationships and summarise data in new and valuable ways for data owners. It is increasingly vital in modern company for translating data into business intelligence and providing an information edge. Data mining's automated, prospective analysis extends beyond the examination of previous events provided by decision support systems' retroactive capabilities. Business problems that formerly took too long to address can now be answered using data mining methods. In both the business and public sectors, data mining is becoming increasingly popular. Data mining is commonly used to cut costs, improve research, and increase sales in industries such as banking, insurance, healthcare, and retail. Data mining is a huge step forward in terms of the types of analytical tools currently available, but it has limitations. One restriction is that data mining aids in the discovery of patterns and linkages, but does not express to the user the value or significance of such patterns. The user must make these kinds of selections. The second flaw is that data mining can uncover patterns of behaviour and correlations between variables, but not necessarily causality. Professional skills and analytical experts who can arrange the analysis and explain the results are required for successful data mining.
Pages: 339-347
Keywords: Discovering Knowledge, OLAP, Extraction, Mining, Data warehouse