PATTERN RECOGNITION
THE SCIENCE OF ANALYSING DATA
Pattern recognition is a component of Data Science that involves the identification and analysis of recurring structures or patterns in data.
PATTERN RECOGNITION TASKS & USES
Pattern recognition uses statistical and machine learning techniques to extract meaningful insights and identify trends, correlations, and anomalies in data. Pattern recognition has numerous practical applications, including fraud detection, detecting trends in signals, and social networks.
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Fraud detection
Pattern recognition can be used to identify fraudulent behaviour by analysing patterns in financial transactions such as credit card fraud or insider trading.
Natural Sciences
Analyse and identify patterns in data to predict and take action in biology to analyse DNA and RNA patterns to study evolution and identify patterns in gene expression data to uncover new biomarkers.
SOCIAL MEDIA
Social network analysis with pattern recognition involves detecting user behaviour patterns in social networks, such as identifying influential users, uncovering hidden communities, or predicting user behaviour. This technique can also be used to identify patterns of misinformation or fake news propagation in online social networks and to analyse patterns in the diffusion of information or trends in online discussions.
KEY OFFERINGS
Why is it a good idea to use Pattern Recognition?
Optimise processes and systems by identifying patterns in data
Identify patterns and trends in data to inform decision-making.
Make predictions from data
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