Key Difference – Data Mining vs Machine Learning
Data mining and machine learning are two areas which go hand in hand. As they being relations, they are similar, but they have different parents. But at present, both grow increasingly like one other; almost similar to twins. Therefore, some people use the word machine learning for data mining. However, you will understand as you read this article that machine language is different from data mining. A key difference is that data mining is used to get rules from the available data while, machine learning teaches the computer to learn and understand given rules.
What is Data Mining?
Data mining is the process of extracting implicit, previously unknown, and potentially useful information from data. Although data mining sounds new, the technology is not. Data mining is the main method of computational disclosure of patterns in large data sets. It also involves methods at the intersection of machine learning, artificial intelligence, statistic and database systems. Data mining field includes data base and data management, data pre-processing, inference considerations, complexity considerations, post-processing of discovered structures, and online updating. Data dredging, data fishing, and data snooping are more commonly referring terms in data mining.
Today, companies use powerful computers to examine large volumes of data and analyze market research reports for years. Data mining helps these companies to identify the relationship among internal factors such as price, staff skills, and external factors such as competition, economic condition, and customer demographics.
What is Machine Learning?
Machine learning is a part of computer science and very similar to data mining. Machine learning is also used to search through the systems to look for patterns, and explore the construction and study of algorithms. Machine learning is a type of artificial intelligence that provides computers the ability to learn without being explicitly programmed. Machine learning mainly targets the development of computer programs that can teach themselves to grow and change according to new situations and it really close to computational statistics. It also has strong ties to mathematical optimization. Some of the most common application of machine learning are spam filtering, optical character recognition, and search engines.
Machine learning is sometimes conflicted with data mining as both are like two faces on a dice. Machine learning tasks are typically classified into three broad categories such as supervised learning, unsupervised learning, and reinforcement learning.
What is the difference between Data Mining and Machine Learning?
How they work
Data Mining: Data mining is a process starting from apparently unstructured data to find interesting patterns.
Machine Learning: Machine learning uses a lot of algorithms.
Data Mining: Data mining is used to extract data from any data warehouse.
Machine Learning: Machine learning is to read the machine which relates to system software.
Data Mining: Data mining mainly utilizes data from a particular domain.
Machine Learning: Machine learning techniques are fairly generic and can be applied to various settings.
Data Mining: Data mining community focuses mainly on algorithms and applications.
Machine Learning: Machine learning communities pays more on theories.
Data Mining: Data mining is used to get rules from data.
Machine Learning: Machine learning teaches the computer to learn and understand given rules.
Data Mining: Data mining is a research area that uses methods like machine learning.
Machine Learning: Machine learning is a methodology that is used to allow computers to do intelligent tasks.
Data Mining vs. Machine Learning
Although machine learning is entirely different with data mining, they are typically similar to each other. Data mining is the process of extracting hidden patterns from large data, and machine learning is a tool that can also be used for that. The field of machine learning further grew as the result of building AI. The data Miners typically have a strong interest in machine learning. Both, data mining and machine learning, collaborate equally for the development of AI as well as research areas.
1. "CRISP-DM Process Diagram" by Kenneth Jensen - Own work. [CC BY-SA 3.0] via Wikimedia Commons
2. "Automated online assistant" by Bemidji State University [Public Domain] via Wikimedia Commons