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Table of contents
1.
Introduction
2.
Data Mining
2.1.
Datasets on which Data Mining can be performed
2.1.1.
Relational Databases:
2.1.2.
Data Warehouses:
2.1.3.
Object-Relational Databases:
2.1.4.
Transactional Databases:
2.2.
Pros of Data Mining
2.3.
Cons of Data Mining
3.
Deep Learning
3.1.
Architectures used in Deep Learning
3.1.1.
Feedforward Neural Networks (FNN):
3.1.2.
Convolutional Neural Networks (CNN):
3.1.3.
Recurrent Neural Networks (RNN):
3.2.
Pros of Deep Learning
3.3.
Cons of Deep Learning
4.
Difference between Data Mining and Deep Learning
5.
Frequently Asked Questions
5.1.
What is Data Mining?
5.2.
What is Deep Learning?
5.3.
What are the types of data mining?
5.4.
What are the types of deep learning?
6.
Conclusion
Last Updated: Mar 27, 2024

Data Mining vs Deep Learning

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Introduction

Both Data Mining and Deep Learning are areas inspired by each other, yet they have different ends. Data mining basically uses the techniques developed by machine learning to predict the outcomes. Whereas deep learning is just a subset of machine learning which works the same way on the machine, like how a brain processes information.

Data Mining vs Deep Learning

In the article “data mining vs deep learning”, we will first understand what is data mining and deep learning with their types, pros, and cons. Then we will discuss the difference between data mining and deep learning.

Data Mining

In the article “data mining vs deep learning”, first we will discuss what is data mining. Data Mining consists of some techniques and tools which are used by scientists to find out the properties of datasets. Data mining uses machine learning to predict the outcomes. Basically, data mining extracts new and possibly information from large sets of data and transforms it into something useful for future use.

For example, If there are three different products of Amazon which are claimed to be bought by the customers frequently together. So here, data mining is used to find this insightful information, and now these products can be clubbed to make it a set so that more customers buy these products.

Datasets on which Data Mining can be performed

Here are the following types of data on which data mining can be performed:

Relational Databases:

These are the databases which is a collection of data sets organized by tables, columns, and records.

Data Warehouses:

It is a technology that basically collects data from different sources within the organisation to provide meaningful business insights.

Object-Relational Databases:

It is a combination of relational and object-oriented database models which supports classes, objects, inheritance, etc.

Transactional Databases:

Transactional Database is a database management system (DBMS) that has a functionality to undo a database transaction. But now, most relational databases have the capability to undo a database transaction.

Pros of Data Mining

  • Data mining helps in the decision-making in an organization.
     
  • Data mining also helps organizations to obtain knowledge-based data.
     
  • The process of data mining is cost-effective.
     
  • The insight information that is extracted from the data sets can be very helpful in terms of business tactics.
     
  • The process of data mining is very quick and makes it easy for the users to analyze the amount of data in a short period of time.

Cons of Data Mining

  • Most data mining application is not easy to operate that needs advanced training to work on.
     
  • The selection of data mining tools is a very challenging task, especially for beginners.
     
  • The data mining techniques are not precise, which may lead to severe consequences in some conditions.
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Deep Learning

In the article “data mining vs deep learning”, now we will discuss what is deep learning. It is just a subset of machine learning that is based on Artificial Neural Network Architecture which works the same way on the machine, like how a brain processes information. Deep learning is generally used for supervised, unsupervised learning as well as reinforcement learning.

Training the model with deep learning takes more time compared to machine learning, and deep learning requires a high-performance computer with GPU.

Architectures used in Deep Learning

Here are the following architectures that are used in deep learning:

Feedforward Neural Networks (FNN):

FNNs are the simplest type of Artificial Neural Network(ANN). FNNs are used for the tasks such as image classification, speech recognition, and NLP (Natural Language Processing).

Convolutional Neural Networks (CNN):

This type of Neural Network is used for image and video recognition tasks.

Recurrent Neural Networks (RNN):

It is a type of neural network that is able to process sequential data like time series and language processing.

Pros of Deep Learning

  • The deep learning algorithms used for image recognition and natural language processing are very high in accuracy.
     
  • The deep learning models can be easily applied to different ranges of tasks, which shows flexibility.
     
  • Deep learning models also improve performance as more data becomes available.
     
  • The deep learning models can scale to handle large and complex datasets, which shows scalability.

Cons of Deep Learning

  • Deep learning models require the high computational resources to train and optimize.
     
  • The deep learning models are treated as the black boxes, which makes it difficult to understand how models work.
     
  • Sometimes, deep learning models require a large amount of labeled data for training.

Difference between Data Mining and Deep Learning

In the article “data mining vs deep learning”, now we will discuss the important part, the difference between data mining and deep learning:

Basis

Data Mining

Deep Learning

Definition Data Mining consists of some techniques and tools which are used by scientists to find out the properties of datasets. It is just a subset of machine learning based on Artificial Neural Network Architecture which works the same way on the machine, like how a brain processes information.
Types Types of data on which data mining can be performed are Relational Databases, Data Warehouses, Object-Relational Databases, and Transactional Database The architectures that are used in deep learning: Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
Relation to Machine Learning It is a basket of techniques and tools implemented by machine learning. It is a subset of machine learning.
Purpose To find out the properties of datasets and the insightful information which can be used by organizations. Deep learning is generally used for supervised, unsupervised learning as well as reinforcement learning.
Accuracy The accuracy of data mining depends on how data is collected and how machine learning produces the results. The deep learning algorithms used for image recognition and natural language processing are very high in accuracy.
Applications Data mining can be used in identifying sales patterns or trends. Deep learning can be used in image segmentation, image classification, language translation, and sentiment analysis.

 

Frequently Asked Questions

What is Data Mining?

Data Mining consists of some techniques and tools which are used by scientists to find out the properties of datasets and produce insightful information which can be used by organizations.

What is Deep Learning?

Deep Learning is just a subset of machine learning based on Artificial Neural Network (ANN) Architecture which works the same way on the machine, like how a brain processes information.

What are the types of data mining?

Here are the following types of data on which data mining can be performed are Relational Databases, Data Warehouses, Object-Relational Databases, and Transactional databases, etc.

What are the types of deep learning?

Here are the following architectures that are used in deep learning are Feed forward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).

Conclusion

Both Data Mining and Deep Learning are areas inspired by each other, yet they have different ends. Data mining is used for producing the insightful information that can be used by the organization for identifying sales patterns or trends. On the other hand, deep learning is just a subset of machine learning that is based on artificial neural networks (ANN). 

In the article “data mining vs deep learning”, we discussed what is data mining along with its types, pros, and cons, what is deep learning with its types, pros, and cons, and the difference between data mining and deep learning.

Here are more articles that are recommended to read:

 

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