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Last Updated: Mar 27, 2024

Data Mining vs Data Snooping

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

Introduction

Both Data Mining and Data Snooping 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 Data Snooping is a statistical mistake that occurs when a researcher looks at the data before having its statistical guidelines.

Data Mining vs Data Snooping

In the article “data mining vs data snooping”, we will first be going to discuss what are data mining and data snooping with their types, pros, and cons. Then we will discuss the difference between data mining and data snooping.

Data Mining

In the article “data mining vs data snooping”, 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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Data Snooping

In the article “data mining vs data snooping”, we will discuss what is data snooping. Data Snooping is a statistical mistake that occurs when a researcher looks at the data before having its statistical guidelines. Data Snooping occurs when a given set of data is used more than once for purposes of inference selection, and these are called the problems of multiple inference.

Data Snooping is done on small data sets without any specific goal, and this is often done by hand (manually), which can lead to fake conclusions.

Pros of Data Snooping

  • Data snooping provides rapid insights into datasets without the need for a predefined research hypothesis
     
  • Data snooping can lead to some unexpected findings that might have not been discovered by the traditional hypothetical approaches
     
  • New research ideas can be generated by analyzing the data with data snooping
     
  • Data snooping also helps the organizations to make data-driven decisions

Cons of Data Snooping

  • The choices of researchers in the analysis methods can introduce bias which may impact the result
     
  • There is a risk of drawing incorrect conclusions with the data snooping
     
  • Models can perform well on the existing data but may fail in new or unseen data

Difference between Data Mining and Data Snooping

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

Basis

Data Mining

Data Snooping

Definition Data Mining consists of some techniques and tools which are used by scientists to find out the properties of datasets.

Data Snooping is a statistical mistake that occurs when a researcher looks at the data before having its statistical guidelines.

Relation to Machine Learning It is a basket of techniques and tools implemented by machine learning. Data snooping is a technique that is part of data science, and data science overlaps with machine learning.
Purpose To find out the properties of datasets and the insightful information which can be used by organizations. To help the organizations to make data-driven decisions with data snooping.
Data Sets Usually, data mining is done with large data sets. Data Snooping is done on small data sets.
Conclusions Data Mining helps in finding true patterns and relationships in data. Data Snooping can lead to false conclusions.

 

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 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 is Data Snooping?

Data Snooping is a statistical mistake that occurs when a researcher looks at the data before having its statistical guidelines, which is done on small data sets and can lead to false conclusions.

What are the pros of data snooping?

It provides rapid insights into datasets without the need for a predefined research hypothesis. It can lead to some unexpected findings that might have not been discovered by the traditional hypothetical approaches. New research ideas can be generated by analysing the data with data snooping.

What are the pros of data mining?

Data mining helps in the decision-making in an organisation and to obtain knowledge-based data. The process of data mining is cost-effective and is very quick which makes it easy for the users to analyse the amount of data in a short period of time.

Conclusion

Both Data Mining and Deep Learning are areas inspired by each other, yet they have different ends. Data mining is used for producing insightful information that can be used by the organization for identifying sales patterns or trends. On the other hand, Data Snooping is a statistical mistake that occurs when a researcher looks at the data before having its statistical guidelines.

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

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Topics covered
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.
Data Snooping
3.1.
Pros of Data Snooping
3.2.
Cons of Data Snooping
4.
Difference between Data Mining and Data Snooping
5.
Frequently Asked Questions
5.1.
What is Data Mining?
5.2.
What are the types of data mining?
5.3.
What is Data Snooping?
5.4.
What are the pros of data snooping?
5.5.
What are the pros of data mining?
6.
Conclusion