Data Mining Methodology

Data Mining:CRISP - Data mining

Data mining can be defined as the process of extracting valid, authentic, and actionable information from large databases using various data mining techniques like machine learning, artificial intelligence (AI) and statistics to derive patterns and trends that exist in data. These patterns and trends can be collected together and defined as a mining model.

Almost all industries these days are taking advantage of this technique including manufacturing, marketing, chemical, aerospace etc. to increase their business efficiency. Therefore the needs of a standard data mining process increased dramatically which should be easy, comprehensive, reliable and uniform across the industry.

Data Mining Methodology:-

As a result in 1990 a Cross Industry Standard Process for Data Mining (CRISP-DM) first published the uniform and standard process for data mining by defining below 7 steps

1. Defining the problem – Understanding Business Process Continue reading


Data Mining Architecture

Data Mining: 

Data mining is described as the process of discovering or extracting interesting and meaningful knowledge from large volume of data which are stored in multiple data sources like databases, file system , data warehouse etc. The knowledge extracted from data mining contributes a lots of benefit to business strategies, scientific & medical research, governments and individual. Data warehouse systems has been designed to provide analytical reports that helps (DSS- Decision Support System) business users to make decisions.

Data Mining Architecture Continue reading