Difference Between Data mining and Machine learning
The process of extracting useful information from a huge amount of data is called Data mining. Data mining is a tool that is used by humans to discover new, accurate, and useful patterns in data or meaningful relevant information for the ones who need it.
The process of discovering algorithms that have improved courtesy of experience derived data is known as machine learning. It is the algorithm that permits the machine to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element.
Below is a table of differences between Data Mining and Machine Learning:
|S.No.||Data Mining||Machine Learning|
|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|