EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Data Mining vs Machine Learning

By Priya PedamkarPriya Pedamkar

Home » Data Science » Data Science Tutorials » Head to Head Differences Tutorial » Data Mining vs Machine Learning

Data mining vs Machine learning

Difference Between Data Mining and Machine Learning

Data mining introduce in 1930 involves finding the potentially useful, hidden and valid patterns from large amount of data. While, machine learning introduced in near 1950 involves new algorithms from the data as well as previous experience to train and make predictions from the models, both of them intersect at the point of having useful dataset but other than that they have various difference based upon the responsibilities, origin, Implementation, Nature, Application, Abstractions, Techniques and scope.

Let us understand Data mining and Machine learning in detail in this post.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Head to Head comparison Between Data mining and Machine learning (Infographics)

Below is the Top 10 Comparision between Data mining and Machine learning:

Data Mining vs Machine learning Infographics

Key Differences Between Data Mining and Machine Learning

Let us discuss some of the major difference between Data Mining and Machine Learning:

  1. To implement data mining techniques, it used two-component first one is the database and the second one is machine learning. The Database offers data management techniques while machine learning offers data analysis techniques. But to implement machine learning techniques it used algorithms.
  2. Data Mining uses more data to extract useful information and that particular data will help to predict some future outcomes for example in a sales company it uses last year data to predict this sale but machine learning will not rely much on data it uses algorithms, for example, OLA, UBER machine learning techniques to calculate the ETA for rides.
  3. Self-learning capacity is not present in data mining, it follows the rules and predefined. It will provide the solution for a particular problem but machine learning algorithms are self-defined and can change their rules as per the scenario, it will find out the solution for a particular problem and it resolves it by its own way.
  4. The main and foremost difference between data mining and machine learning is, without the involvement of human data mining can’t work but in machine learning human effort is involved only the time when algorithm is defined after that it will conclude everything by own means once implemented forever to use but this is not the case with data mining.
  5. The result produces by machine learning will be more accurate as compared to data mining since machine learning is an automated process.
  6. Data mining uses the database or data warehouse server, data mining engine and pattern evaluation techniques to extract the useful information whereas machine learning uses neural networks, predictive model and automated algorithms to make the decisions.

Data mining and Machine learning Comparison Table

Below are the lists of points, describe the comparison between Data Mining and Machine Learning

Basis for Comparison Data Mining Machine Learning
Meaning Extracting knowledge from a large amount of data Introduce a new algorithm from data as well as past experience
History Introduced in 1930, initially referred as knowledge discovery in databases Introduced in near 1950, the first program was Samuel’s checker-playing program
Responsibility Data mining is used to get the rules from the existing data. Machine learning teaches the computer to learn and understand the given rules.
Origin Traditional databases with unstructured data Existing data as well as algorithms.
Implementation We can develop our own models where we can use data mining techniques for We can use machine learning algorithm in the decision tree, neural networks and some other area of artificial intelligence.
Nature Involves human interference more towards manual. Automated, once design self-implemented, no human effort
Application used in cluster analysis used in web search, spam filter, credit scoring, fraud detection, computer design
Abstraction Data mining abstract from the data warehouse Machine learning reads machine
Techniques Involved Data mining is more of research using methods like machine learning Self-learned and trains system to do the intelligent task.
Scope Applied in the limited area Can be used in a vast area.

 Conclusion

In most of the cases now data mining is used to predict the result from historical data or find a new solution from the existing data. Most of the organization uses this technique to drive the business outcomes. Where machine learning techniques are growing in a much faster way since it overcomes the problems with what data mining techniques have. Since Machine learning process is more accurate and less error prone when compared to data mining and it is much more capable to take his own decision and resolve the issue. But to drive the business still, we need to have data mining process because it will define the problem of a particular business and to resolve such problem we can use machine learning techniques. In one word we can say that to drive a business both Data mining and Machine learning techniques have to work hand to hand, one technique will define the problem and other will give you the solution in the much accurate way.

Popular Course in this category
Sale
Machine Learning Training (19 Courses, 29+ Projects)19 Online Courses | 29 Hands-on Projects | 178+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.7 (13,745 ratings)
Course Price

View Course

Related Courses
Data Scientist Training (85 Courses, 67+ Projects)Tableau Training (7 Courses, 8+ Projects)Azure Training (6 Courses, 5 Projects, 4 Quizzes)Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)Data Visualization Training (15 Courses, 5+ Projects)All in One Data Science Bundle (360+ Courses, 50+ projects)

Recommended Articles

This has been a guide to Data Mining vs Machine Learning. Here we have discussed Data Mining vs Machine Learning head to head comparison, key difference along with infographics and comparison table. You may also look at the following articles to learn more –

  1. 8 Important Data Mining Techniques for Successful Business
  2. 7 Important Data Mining Techniques for Best results
  3. 5 Best Difference Between Big Data Vs Machine Learning
  4. 5 Most Useful Difference Between Data Science vs Machine Learning

Machine Learning Training (17 Courses, 27+ Projects)

19 Online Courses

29 Hands-on Projects

178+ Hours

Verifiable Certificate of Completion

Lifetime Access

Learn More

11 Shares
Share
Tweet
Share
Primary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • PostgreSQL vs MariaDB
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Datadog vs Splunk
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Special Offer - Machine Learning Training (17 Courses, 27+ Projects) Learn More