1. |
Extracting useful information from large amount of data |
Introduce algorithm from data as well as from past experience |
2. |
Used to understand the data flow |
Teaches the computer to learn and understand from the data flow |
3. |
Huge databases with unstructured data |
Existing data as well as algorithms |
4. |
Models can be developed for using data mining technique |
machine learning algorithm can be used in the decision tree, neural networks and some other area of artificial intelligence |
5. |
human interference is more in it. |
No human effort required after design |
6. |
It is used in cluster analysis |
It is used in web Search, spam filter, fraud detection and computer design |
7. |
Data mining abstract from the data warehouse |
Machine learning reads machine |
8. |
Data mining is more of a research using methods like machine learning |
Self learned and trains system to do the intelligent task |
9. |
Applied in limited area |
Can be used in vast area |
10. |
Uncovering hidden patterns and insights |
Making accurate predictions or decisions based on data |
11. |
Exploratory and descriptive |
Predictive and prescriptive |
12. |
Historical data |
Historical and real-time data |
13. |
Patterns, relationships, and trends |
Predictions, classifications, and recommendations |
14. |
Clustering, association rule mining, outlier detection |
Regression, classification, clustering, deep learning |
15. |
Data cleaning, transformation, and integration |
Data cleaning, transformation, and feature engineering |
16. |
Strong domain knowledge is often required |
Domain knowledge is helpful, but not always necessary |
17. |
Can be used in a wide range of applications, including business, healthcare, and social science |
Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity |