We can simply define data mining as a process that involves searching, collecting, filtering and analysing the data. It is important to understand that this is not the standard or accepted definition. But the above definition caters to the whole process.

A large amount of data can be retrieved from various websites and databases. It can be retrieved in form of data relationships, co-relations, and patterns. With the advent of computers, internet, and large databases it is possible to collect large amounts of data. The data collected may be analysed steadily and help identify relationships and find solutions to the existing problems.

Governments, private companies, large organizations and all businesses are after a large volume of data collection for the purposes of business and research development. The data collected can be stored for future use. Storage of information is quite important whenever it is required. It is important to note that it may take a long time for finding and searching for information from websites, databases and other internet sources.

Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.

Data mining and its process

Data mining is an interactive process. Take a look at the following steps.

• 1.Requirement gathering

  Data mining project starts with the requirement gathering and understanding. Data mining analysts or users define the requirement scope with the vendor business perspective. Once, the scope is defined we move to the next phase.

• 2.Data exploration

  Here, in this step Data mining experts gather, evaluate and explore the requirement or project. Experts understand the problems, challenges and convert them to metadata. In this step, data mining statistics are used to identify and convert the data patterns.

• 3.Data preparations

  Data mining experts convert the data into meaningful information for the modelling step. They use ETL process – extract, transform and load. They are also responsible for creating new data attributes. Here various tools are used to present data in a structural format without changing the meaning of data sets.

• 4.Modelling

  Data experts put their best tools in place for this step as this plays a vital role in the complete processing of data. All modeling methods are applied to filter the data in an appropriate manner. Modelling and evaluation are correlated steps and are followed same time to check the parameters. Once the final modeling is done the final outcome is quality proven.

• 5.Evaluation

  This is the filtering process after the successful modelling. If the outcome is not satisfied then it is transferred to the model again. Upon final outcome, the requirement is checked again with the vendor so no point is missed. Data mining experts judge the complete result at the end.

• 6.Deployment

  This is the final stage of the complete process. Experts present the data to vendors in the form of spread sheets or graphs.