The ultimate guide to data mining

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Do you know? 2.5 quintillion bytes of data is produced by humans everyday! By 2022 our gathered digital universe of data will increase from 4.4 zettabytes to 44 zettabytes!

And 1.7 mb of information will be produced per second by every human being on the planet!

The messages we send on WhatsApp and even this article you are reading right now contributes to a certain amount of data. But have you ever wondered how google feed shows data of our interests only? How does Netflix recommend movies similar to those we’ve watched earlier?

Well the answer to these questions my friend, is Data Mining! and we will understand what it means in this article of terminal stack.

What is Data Mining?

Data mining refers to extraction of knowledge from a large amount of data. It is a semi automatic or automatic process that inspects large amounts of information to discern similar trends and patterns.

This branch of data science derives its name from the similarities between searching for valuable information in a large set of databases and mining a mountain for ore. As both processes require searching a tremendous amount of material to find hidden value.

It is also known as KDD (knowledge discovery in data)

How does Data mining work?

The Data mining process may be broken down into four main stages

  • Data gathering: Useful data for an analytical application is identified. The info could be located in various data systems such as data warehouses. Structured and unstructured data can also be collected from unknown sources and is often moved to a data lake by a data scientist for further steps in this process.
  • Data preparation: The data we have got in the previous stage is prepared to be mined here. It involves data exploration, profiling and pre-processing. Data transformation is likewise performed to make data units consistent and error free.  
  • Mining the data: Once the data is prepared a data scientist selects a particular mining algorithm to search data. In machine learning applications the data is basically tested on a sample before it is applied against a full set of data.
  • Data analytics and interpretation: The results obtained from above processes helps in decision making and other business actions. The data scientists then present this information to executives through data visualisations.
how does data mining work

Key Data Mining concepts

Data mining works on certain techniques which are listed as follows:

  • Data cleansing and preparation: In this step, the data is transformed into a suitable form for further analysis i.e. to rectify errors and to find missing data.
  • Artificial intelligence: Here, analytical activities such as planning, problem solving, learning, reasoning etc are performed by AI systems. 
  • Association rule learning: This involves use of market basket analysis which helps to form relationships among various factors in a dataset to determine which products are typically purchased together.
  • Clustering : It is a process in which a dataset is divided  into subclasses called clusters, to help users understand the natural grouping of data.
  • Classification: This method basically allots things in a dataset to target groups or classes with the objective of precisely foreseeing the target class for each case in the information.
  • Data Analytics :This involves assessing advanced data into valuable business insight.
  • Data warehousing: An enormous collection of business information used to help an organisation to make decisions. 
  • Regression: A procedure used to anticipate a range of numeric values, like sales, temperatures, or stock prices, in view of a specific data index.

Advantages of data mining

  • Data mining helps companies to make profitable changes to operations and production.
  • It helps organisations to create targeted marketing and advertising campaigns. Likewise the sales team can use the result obtained from these procedures to make lead conversions.
  • Enables automated discovery of hidden patterns and trends 
  • It is a fast process which helps new users to investigate significant quantities of information in a brief time.
  • Helps in quick decision making process
  • Risk managers and financial executives can now investigate fraudulent activities and develop strategies for managing them

Disadvantages of data mining

  • Sometimes businesses may sell useful data of clients to other organisations for money.
  • Certain data mining softwares need advanced trainings to work on
  • Selection of certain data mining technique is a bit challenging task because every technique works on different algorithm used in their design 
  • The data mining techniques are not exact. So it may lead serious consequences in specific conditions 

Applications of data mining 

The predictive nature of data mining has caused the business prospects to detect consumer behaviour, change business strategies, decide value, sway on deals and a lot more.

Following are the areas where data mining is used:

application of data mining

Healthcare:

Data mining has enormously boosted the health care sector. It uses data for better insights and to find practices which are more beneficial and cost efficient. Analysts use various data mining approaches like multi dimensional databases, data visualisation, machine learning and statistics etc. to predict more accurate diagnostic procedures by analyzing patients medical history,physical examination and treatment procedures etc.

Data mining also helps health insurers to prevent fraud and malpractices

Data mining in Market Basket Analysis

Market Basket Analysis is a method based on hypothesis. It is based on consumer behavior. For example if a customer is buying a particular set of products, he or she is more likely to buy that same set of products again. This technique helps businesses to understand the purchase behaviour of a buyer and they can recommend similar types of products next time. 

Financial services 

Banks and credit card companies use data mining tools to calculate risk and analyse fraudulent transactions,purchasing patterns and customer’s financial data. It also allows banks to keep a track on our online preferences to optimise the return on their marketing campaigns.

Insurance 

Insurers rely on data mining processing to assist in valuation of insurance policies and whether to approve policy applications 

Entertainment 

OTT (over-the-top) media services like netflix, amazon prime etc use data mining to analyse what users are watching or listening to create personalised recommendations based on users habit. 

Difference between Data mining and big data

Big data refers to the collection of large data assets. However data mining helps to identify and extract relevant information from big data.

data mining vs big data

Conclusion

Data mining is utilized in different applications like banking, advertising, medical services, telecom ventures, and numerous different regions.

Data mining procedures assist organizations with acquiring learned data, and increase their productivity by making changes in cycles and tasks. It is a quick interaction which helps business in decision making through investigation of stowed away examples and patterns.

References 

https://www.ibm.com/cloud/learn/data-mining

https://www.sas.com/en_us/insights/analytics/data-mining.html

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