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Table of contents
1.
Introduction
2.
Understanding Data Mining
2.1.
Powerful Trio of Data Mining
2.2.
Data Mining Process
2.2.1.
Define business goals 
2.2.2.
Data understanding
2.2.3.
Pattern Identification
2.2.4.
Accuracy
2.2.5.
Apply findings
2.3.
Data Mining Methods and Algorithms
2.3.1.
Classification
2.3.2.
Regression
2.3.3.
Clustering
2.3.4.
Neural Networks
2.3.5.
Association rule
2.3.6.
Sequential patterns
2.3.7.
Outer detection
3.
Understanding Data Profiling
3.1.
Data Profiling Process
3.2.
Types of Data Profiling
3.2.1.
Structure Discovery
3.2.2.
Content Discovery
3.2.3.
Relationship Discovery
3.3.
Data Profiling Methods
4.
Data Mining vs Data Profiling
5.
Frequently Asked Questions
5.1.
What is data mining?
5.2.
What is Data Profiling?
5.3.
What is the key difference between Data Mining vs Data Profiling?
5.4.
What is the powerful trio of data mining?
6.
Conclusion 
Last Updated: Mar 27, 2024

Data Mining vs Data Profiling

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Introduction

In today's world of data, businesses want to get useful information from big data sets. They use two main methods: data mining and data profiling. While Data Mining is the process of discovering patterns and connections in large data sets, Data Profiling is analyzing data characteristics to understand its quality and structure.

Data Mining vs Data Profiling

In this article, we will be discussing the key features and applications of Data Mining and Data profiling, followed by a table on Data Mining vs Data Profiling.

Understanding Data Mining

Data Mining is the process of discovering trends, patterns, connections, and important information from big sets of structured data. It uses different tools and algorithms to process data and find their patterns, which businesses later use in data-based decision-making.

Powerful Trio of Data Mining

Data Mining is like a blend of three scientific fields: StatisticsArtificial Intelligence, and Machine Learning. While statistics help analyze the datasets, the power of AI is used to get data from different systems, and machine learning is used to build models based on the different data mining methods.

Together the three help to find useful information from large data sets.

Data Mining Process

The Data Mining process is divided into five broad steps:

Define business goals 

Set clear goals to be achieved within a definite time period. These goals provide a direction for the organization of the company resources. Business goals can include revenue targets, customer satisfaction, innovation, etc.

Data understanding

Data understanding is an important step in data mining. It is the first step of data mining, where we use statistics to understand and modify the characteristics of the dataset. By understanding the data, data analysts and data scientists can make informed decisions on how to preprocess, clean, and transform the data to ensure it is suitable for further analysis.

Pattern Identification

Pattern identification means finding meaningful relationships between different data points that exist together in the database. This step involves applying different data mining methods, such as clustering, classification, regression, and association rule mining, to get valuable patterns and insights. 

Accuracy

Check the degree of correctness of the results obtained from data analysis. High accuracy in data analysis ensures that business decisions are based on reliable information, reducing the risk of making costly mistakes.

Apply findings

It is the process by which the results of the data analysis obtained by the machine learning models are applied in real-life businesses to make better decisions. This involves integrating the data-driven discoveries into real-life business processes, actions, and decision-making frameworks. 

Data Mining Methods and Algorithms

Data Mining can be performed using many methods and algorithms, such as:

Classification

It is a process of sorting things into different groups based on their characteristics or features. Common algorithms used for classification include Decision Trees, Random Forests, Support Vector Machines (SVM), Naive Bayes, and k-Nearest Neighbors.

Regression

Regression is a data mining method focused on modelling the relationship between a dependent variable (target) and one or more independent variables (predictors) that change together in a smooth and continuous manner. 

Clustering

It is like putting similar things in a big group without knowing the groups beforehand. It helps find patterns in a large dataset by identifying similar data points and grouping them together. This is quite helpful in customer segmentation, error detection, etc.

Neural Networks

It is a set of machine learning algorithms that work like a human brain to understand the connections and relationships between the data. Neural Networks are used for many tasks like image recognition, natural language processing, and pattern classification.

Association rule

Association Rule is a method that uses machine learning models to look for patterns in datasets. It helps in designing catalogues, cross-marketing, and understanding the shopping behaviour of customers to make smart decisions by keeping a track of the co-occurring items in the data set. 

Sequential patterns

Sequence patterns are a data mining method used to analyze data that appears in a sequence.

Outer detection

It is the data mining approach that looks at data points that behave differently than the expected pattern. It is generally used to detect fraud.

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Understanding Data Profiling

Data Profiling is the process of analyzing a dataset to learn more about it. It is understanding the details of the data, patterns, content, and quality to make better use of it for many purposes. 

Data Profiling Process

The Data Profiling process can be divided into the following steps:

1. Find reliable data: Look for accurate and reliable data to perform data profiling.
 

2. Identify the data quality issues: Find the problems in the data related to quality.
 

3. Use the ETL process: Use the Extract, Transform, and Load (ETL) process to find the data quality issues.
 

4. ETL rules: Run the ETL process following the relationships, hierarchical structures, and ETL rules to achieve data accuracy.

Types of Data Profiling

There are three types of data profiling:

Structure Discovery

Structure discovery uses numerical calculations to analyze the arrangement and format of data.

Content Discovery

This type of data profiling focuses on finding errors within the data elements, such as identifying the rows in a table containing issues.

Relationship Discovery

It analyzes the data to find the connections between the different elements in the data.

Data Profiling Methods

The are many data profiling methods, such as:

  • Column profiling: Column profiling checks each column in a table to see how often different values appear, helping to find patterns and frequencies in the data.
     
  • Cross-column profiling: This method looks at relationships between columns in a table. It finds the primary key and analyzes the patterns within the data.
     
  • Data rule validation: This method makes sure that the data in the dataset follows the rules set beforehand.
     
  • Cross-table profiling: Cross-table profiling analyzes the relationships between different tables.
     
  • Key Integrity: This method makes sure that all the required keys are present in the data and marks any key-related issues.
     
  • Pattern and frequency distribution: This method makes sure that all the data fields are correctly formatted and keeps track of how often the data patterns appear in the data. 
     
  • Cardinality: Cardinality divides the relationships between one-to-one and one-to-many relationships.
     

Next Let us study a table on the key differences between Data Mining vs Data Profiling.

Data Mining vs Data Profiling

The main differences between Data Mining vs Data Profiling are:

Criteria

Data Mining

Data Profiling

Definition Data Mining is the process of identifying patterns in a database. Data Profiling is the process of analyzing the characteristics and quality of data from the existing dataset.
Other name Knowledge Discovery in Databases (KDD) Data Archaeology
Purpose Build machine learning models.  Provides accuracy, consistency, and error-free data within the dataset.
Data Types It works on structured data. It works on both structured and unstructured data.
Methods It involves methods like classification, clusteringregression, association rule, neural networks, etc. It involves methods like column profiling, cross-column profiling, cross-table profiling, cardinality, key integrity, etc.
Tools used Tools used in Data Mining are Weka, KNIME, Rattle, SPSS, Data Melt, Orange, Sisense, RapidMiner, etc. Tools used in Data Profiling are Atlan, IBM Infosphere Information Analyzer, Melissa Data Profiler, Aggregate Profiler, etc.
Applications It is used in customer behavior analysis, business intelligence, fraud detection, etc. It is used in targeted advertising, fraud detection, delivery logistics, etc.

Also see, Difference Between Data Analyst and Business Analyst

Frequently Asked Questions

What is data mining?

Data Mining refers to the process of discovering patterns, connections, and important information from big data sets. It uses different tools and algorithms to process data and find their patterns, which businesses later use in data-based decision-making.

What is Data Profiling?

Data Profiling is the process of analyzing a dataset to learn more about it. It involves understanding the details of the data, patterns, content, and quality to make better use of it for many purposes. 

What is the key difference between Data Mining vs Data Profiling?

Data mining involves finding patterns and useful information from big datasets, while data profiling focuses on analyzing the quality and properties of data to understand its structure and issues.

What is the powerful trio of data mining?

The powerful trio of Data Mining is Statistics, Artificial Intelligence, and Machine Learning. While statistics help analyze the datasets, the power of AI is used to get data from different systems, and machine learning is used to build models based on the different data mining methods.

Conclusion 

Kudos on finishing this article! We have discussed how data mining and profiling are effectively used in data analysis. 

We hope this blog has helped you understand the prime differences between Data Mining vs Data Profiling.

Keep learning! We suggest you read some of our other articles related to Data Analysis: 

  1. Introduction to data mining
  2. Types of data mining techniques
  3. Tools in Data Mining

 

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