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Last Updated: Mar 27, 2024
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Data Mining vs Machine Learning

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Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

As we move into the era of big data and artificial intelligence, Data Mining and Machine Learning have emerged as powerful tools for extracting knowledge from vast amounts of data. Although they have common goals and often work together, they are distinct fields with unique methods and applications.  In this article, we will cover the differences between data mining and Machine Learning.

Data Mining vs Machine Learning

Understanding Data Mining

Data Mining is the practice of automatically searching large stores of data for patterns and trends. It's about discovering hidden nuggets of information that can help businesses make data-driven decisions.

How Does Data Mining Work?

The data mining process involves several key steps:

  • Data Cleaning: Remove noise and irrelevant data
     
  • Data Integration: Combine multiple data sources
     
  • Data Selection: Choose the relevant data for analysis
     
  • Data Transformation: Convert data into a format suitable for mining
     
  • Data Mining: Apply data mining techniques to extract patterns
     
  • Pattern Evaluation: Identify truly meaningful patterns
     
  • Knowledge Representation: Present the mined knowledge to users

Technique

One popular data mining technique is the Apriori algorithm, used for mining frequent itemsets and relevant association rules. It is traditionally used in market basket analysis to identify items that are bought together.

  • Python

Python

# Simplified Python code for the Apriori algorithm

def apriori(data, minSupport):

    candidateSet = createCandidateSet(data)

    freqSet = getFrequentSet(candidateSet, minSupport)

    rules = generateRules(freqSet, minSupport)

    return rules
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Delving into Machine Learning

Machine Learning, a branch of artificial intelligence, is the science of getting computers to learn and act like humans do, improving their learning over time autonomously by feeding them Data and Information in the form of observations and real-world interactions.

How Does Machine Learning Work?

The Machine Learning process involves several steps:

  • Data Collection: Gather the data that you'll feed to the model
     
  • Data Preparation: Clean and preprocess the data
     
  • Choose a Model: Select the machine learning algorithm that will learn from your data
     
  • Train the Model: Feed your data to the model
     
  • Evaluate the Model: Assess how well your model is performing
     
  • Parameter Tuning: Adjust your model to improve its performance
     
  • Make Predictions: Use your model to make predictions on new data

Technique

A common machine learning technique is linear regression, which predicts a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output).
 

  • Python

Python

# Simplified Python code for Linear Regression

from sklearn.linear_model import LinearRegression

def linear_regression(data, labels):

    model = LinearRegression()

    model.fit(data, labels)

    return model

Data Mining vs Machine Learning: The Differences

Aspects Data Mining Machine Learning
Purpose  To discover hidden patterns and correlations in large datasets To learn from data and make predictions or decisions
Method  Uses mathematical algorithms and statistical techniques Utilizes machine learning algorithms
Applications  Marketing, banking, healthcare, retail, and business intelligence across a range of industries Used in fraud detection, driverless vehicles, natural language processing, recommendation systems, and image recognition.
 Human intervention More human direction and intervention are used in the selection of the data and the evaluation of the results. Data preparation requires human intervention.
Objective Finding hidden connections, correlations, and patterns in the data is the goal. Making forecasts, choices, or suggestions based on new, unforeseen data.

Frequently Asked Questions

Are Data Mining and Machine Learning the same thing?

While they both deal with data, they are not the same. Data Mining is about discovering unseen patterns, while Machine Learning is about learning from data and making predictions.

Can one be used without the other?

Yes, but they work best when used together. Data Mining can help identify patterns which can be useful for machine learning, and machine learning can predict future instances based on the patterns identified by data mining.

Which one should I use?

It depends on your goal. If you want to discover patterns and gain insights from large datasets, use data mining. If you want to make predictions based on those insights, use machine learning.

Conclusion

Although Data Mining and Machine Learning might seem similar at first glance, they are indeed different. Data Mining is about finding valuable information in data, while Machine Learning focuses on using data to make predictions or decisions. By understanding these differences and how these techniques can work together, you can extract more value from your data and make better decisions.

You can also consider our Machine Learning Course to give your career an edge over others.

Topics covered
1.
Introduction
2.
Understanding Data Mining
2.1.
How Does Data Mining Work?
2.2.
Technique
2.3.
Python
3.
Delving into Machine Learning
3.1.
How Does Machine Learning Work?
3.2.
Technique
3.3.
Python
4.
Data Mining vs Machine Learning: The Differences
5.
Frequently Asked Questions
5.1.
Are Data Mining and Machine Learning the same thing?
5.2.
Can one be used without the other?
5.3.
Which one should I use?
6.
Conclusion