Data Mining

In A Level Computer Science, data mining is when large quantities of data are turned into useful information so that patterns can be found
It can be used to search for relationships and facts that are probably not immediately obvious to people
It will extract valuable insights from large sets of data using algorithms and statistical methods
Data mining is used in many fields, including retail, healthcare, and finance, to make informed decisions
The diagram below shows useful business insights that can be gained from data collected by an online grocery business

data-mining How data mining can be used to generate insights

Benefits	Drawbacks
Data mining can be used to identify patterns and trends that may not be immediately obvious to humans.	It requires very powerful computers with a lot of processing power.
It can help organisations make better future predictions.	Inaccurate data can produce inaccurate results.
Organisations can ensure demand is met during busy periods to stay ahead of local competition.	Although it may spot patterns and trends, it may not explain the reasons why these exist.

Data mining algorithms can be used to analyse purchase history and browsing behaviour to provide customised product suggestions
Online retailers like Amazon use purchase data to suggest items for customers based on past activity

Data from healthcare records and other sources can be analysed to predict disease outbreaks or patient admissions
Hospitals use data mining to anticipate flu cases in the coming winter, enabling better resource allocation

Machine learning models trained on historical data can be used to identify suspicious activities among millions of transactions
Credit card companies use data mining algorithms to flag potentially fraudulent transactions in real-time

Data collected from vehicle sensors can be used to predict when a part is likely to fail, enabling more proactive maintenance
Manufacturers like Tesla collect data from electric cars to anticipate when a battery or other components may fail

Data mining helps understand viewer preferences and behaviour, enabling better content recommendations
Streaming services like Netflix use data mining to target new shows and movies to specific audiences based on their previous viewing history

Data mining requires knowledge of complex algorithms for data sorting, pattern recognition, and anomaly detection
Running data mining algorithms within a company requires significant maintenance and expertise
Companies must be careful with customer data and must ensure all mining follows the General Data Protection Regulation (GDPR)
Specialist data engineers and data scientists are in short supply in industry

CS Notes