Data Mining: Definition, Process, Techniques and Tools

What is data mining?

Analysing large sets of data to find out useful patterns is known as data mining. It is all about finding previously unknown relationships within different sets of data. Data mining needs multi-disciplinary skill sets such as machine learning, statistics, AI and database technology.

Data mining can be used for marketing, fraud detection and scientific discoveries. It is also known as knowledge discovery, knowledge extraction, data analysis or pattern analysis.

Types of data required for data mining

• Relational databases
• Advanced databases and information repositories
• Transactional databases
• Spatial databases
• Legacy databases
• Multimedia and streaming data
• Text databases
• Web databases

Implementation of data mining process

• Goal Setting: In the first phase the business understanding and goal of data mining are established.
• Data Check: In the second phase the data is checked for quality and type to make sure that it is fit for data mining.
• Preparing Data: In this step the data is made production ready by selecting, cleaning, formatting and constructing it according to the set parameters.
• Data transformation: In step four, the data is smoothened, to reduce noise, aggregated, generalised, normalised and constructed as per the attributes that help in sound data mining.
• Modelling: In this step the data is modelled to detect any patterns or behaviour that matches the goals or appears otherwise.
• Evaluation: In the next step, the patterns are compared with business objectives for reaching a meaningful conclusion.
• Deployment: The identified data is used for decision making, developing new products and services, or for improving customer experience and various other uses.

Benefits of data mining

• Data mining helps businesses make sound decisions based on knowledge and insights.
• Data mining can help increase the profitability of business operations.
• Data mining is cost-effective and more accurate than other statistical data applications and approaches.
• Data mining can facilitate predictive analysis and lead the way for future growth and development of new products and services.
• Data mining supports both legacy and modern databases, hence benefitting every sphere.
• Data mining can help convert raw data into something meaningful and profitable.

Today, data mining is being used in communications, insurance, education, manufacturing, banking, retail, service industry, e-commerce, crime investigations and bioinformatics. The possibilities are endless.

Read More: Extracting Knowledge from Big Data: What you Need to Know

Outsource data mining

Are you looking for data mining services? If you don't have the resources to build a data mining infrastructure, outsourcing your requirements may be the best choice. Nowadays you can find offshore data mining companies that can help the process of Agile development and deployment in your company. Online IT marketplaces like IT Exchange offer a very lucrative, cost-effective and hassle-free outsourcing experience. You can sign digital contracts and share your requirements, and
IT Exchange will take care of the rest.

To know more about outsourcing software development and offshore data mining services, visit www.itexchangeweb.com.