What Is Pattern Recognition?

Pattern recognition is an automated process thanks to the availability of computer power to ingest data, process it, recognize its patterns and share it for further analysis. Here’s how pattern recognition works.

Written by Edoardo Romani
Published on Apr. 11, 2023
What Is Pattern Recognition?
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Pattern recognition is a process for automating the identification and exploration of patterns in data sets. Since there’s no single way to recognize data patterns, pattern recognition ultimately depends on:

  • The ultimate goal of any given pattern recognition workflow
  • The type of data available (quantitative vs. qualitative, time series data vs. point-in-time data)
  • The computing power and storage available to process and manage the data

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How Pattern Recognition Works

Pattern recognition is a process made of the same steps that anyone concerned with finding patterns in data goes through.

Pattern Recognition Process

  1. Define the problem
  2. Be aware of the null hypothesis
  3. Choose a methodology
  4. Measure uncertainty
  5. Test and iterate over the results


1. Define the Problem

Defining the problem is always the first step in any pattern recognition project. This is where you formulate research questions or hypotheses regarding the data and its patterns. For example, you may be concerned with capturing holiday and seasonal effects (patterns) in shopping data coming from shopping malls’ databases. A specific question we may want to ask about this data is whether shoppers tend to display sensitive responses to specific promotions or discounts the company launches through email marketing campaigns and whether these tend to distribute in any particular way throughout the year.


2. Be Aware of the Null Hypothesis 

In the field of statistics and hypothesis testing, searching to prove the existence of a relationship between variables and finding none is called accepting the null hypothesis. Not all data may have patterns hidden within it. Moving into the analysis, it’s important to remember that the process of pattern recognition may also not yield results. That is to say, you may be looking for patterns where there simply are none.


3. Choose a Methodology

There are many different ways to find patterns and it’s important to evaluate all potential models that may apply to the problem at hand. After all, there may be more than one.

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4. Measure Uncertainty 

Models used to find data patterns are as accurate as they can be within an uncertain world. It’s important to treat pattern recognition under a probabilistic lens to factor in uncertainty, especially when pattern recognition is put to use for predictive purposes. 


5. Test and Iterate Over the Results

Constant iteration over pattern recognition processes is necessary to ensure optimal results and avoid losing relevance or accuracy as time passes. Once you’ve landed on a problem and model, and measured patterns, it’s important to remember that the workflow does not stop there. 

Keep testing pattern recognition methods to make sure they accurately capture trends in the underlying data even as time and conditions go on.

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Features of Pattern Recognition 

Pattern recognition has several applications, but there are a few key tenets that are common regardless of the domain.


Statistical Approach

Pattern recognition is rooted in statistics. When we’re finding patterns in data, we always need to account for variability, uncertainty and the probable distributions, if any, that data holds.

The field of statistics is also the precursor to modern pattern recognition approaches. As a result, a statistical lens is appropriate for most, if not all, modern pattern recognition applications.

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Algorithmic Nature

An algorithm is a procedure that follows a precise sequence of steps. Depending on the nature of the problem and the kind of data at hand, you can use many different algorithms.

The main groups of algorithms used for pattern recognition include:

  • Classification Algorithms
  • Clustering
  • Ensemble Learning
  • Regression Algorithms
  • Sequence Labeling Methods


Data Categorization

While you’re defining a pattern recognition project’s problem, your main concern is usually fitting the data into specific categories, or labels, that are linked to the underlying patterns the data holds.

For example, in time series data analysis, you may be most concerned with understanding the seasonal component of monthly sales data, a category specific to the seasonal pattern you see in the data. You might see sales spikes during the Christmas holiday season.


Reliance on Abundant Data and Processing Power

Pattern recognition has become increasingly prevalent since the technological advances in computing started around the turn of the 21st century. With these advances we can:

  • process more data
  • process data faster (given equal data size) thanks to making use of grid computing, which is the use of many different computers to distribute the computational load across a higher number of servers
  • store data less expensively thanks to the rise of modern cloud database management solutions


Advantages of Pattern Recognition

High Automation Potential

Pattern recognition workflows have the benefit of being a great fit for full end-to-end automation. This means we can configure, program and structure pattern recognition workflows to run with minimal human intervention, once we’ve completed the initial setup and analysis.

In other words, teams developing pattern recognition solutions can benefit from a low-touch, high-return analytical workflow. 



Automation also brings an additional advantage, which is letting subject-matter experts focus on the least intuitive and most complex parts of pattern recognition problems. This is resource-efficient because it brings down the cost of labor and overall time dedicated to developing solutions.

Most organizations can also benefit from plug-and-play situations wherein they simply translate similar pattern recognition problems to their domain with minimal effort. Examples of this include re-using code and/or algorithms already developed by others, especially if they’re available from open-source projects.


Applications for Descriptive and Predictive Analytics 

Pattern recognition is incredibly flexible because it can be used to extract trends from historical data and diagnose what happened in the past (descriptive pattern recognition). We can also use pattern recognition methodologies to make inferences about the future (predictive pattern recognition).

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Examples of Pattern Recognition

Cybersecurity and Voice Detection

A cybersecurity company selling digital security services to client firms can use pattern recognition to develop software that automatically recognizes who is speaking from audio files coming from employee phone calls. We can then use this technology for any number of applications where there may be a use case for monitoring professional phone calls for security or training purposes.


Healthcare Technology and Medical Diagnosis

A medical institution is concerned with helping doctors in identifying early-stage cancer development. Using pattern recognition and a set of digital images, the organization can detect early-stage cancer with high probability, thereby helping patients receive earlier treatment with a higher probability of success.


Marketing and Customer Churn Prevention

A grocery store chain is interested in monitoring its base of loyalty card customers for early indications of customer attrition. The company is interested in this information so it can react promptly by offering incentives and additional offers to these customers to stop them from churning.

We can also put pattern recognition algorithms to good use on the chain’s customer data set to cluster them into different levels of churn probability and identify the churn prevention initiative’s target customers.


Applications of Pattern Recognition

Computer Vision

Pattern recognition methodologies are incredibly popular in computer vision. We can put pattern recognition methodologies to use to programmatically develop applications that derive knowledge from images, and effectively understand them as a human being might.


Machine Learning

Machine learning, a subset of data science, makes use of computing power to derive insights from data using specific learning algorithms. This is one of the most prevalent current applications of pattern recognition and is at the heart of the advancements in AI development in most industries. 


Time Series Analysis

Time series data is essentially logs of data over time. Historical stock prices are an example of time series data. You might also think about sensor and telemetry data from video cameras.

Pattern recognition is key to understanding, analyzing, and even forecasting time series data. This is because time series data is filled with different components (or patterns) that are useful to extract and understand to make sense of the data.

Examples of these time series data components are seasonal effects (such as the ones determined by the Black Friday shopping season for example) and cyclical effects (longer-term trends, such as the steady growth in the value of the stock market).

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