Difference Between Data Mining and Machine Learning

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. Akey 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 isthe 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 snoopingare more commonly referring terms in data mining.

今天,公司使用强大的计算机来检查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?

机learning is a part of computer science and very similar to data mining. Machine learning is also used tosearch through the systems to look for patterns, and explore the construction and study ofalgorithms. 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.

机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 assupervised learning, unsupervised learning, and reinforcement learning.

What is the differencebetween Data Mining and Machine Learning?

How they work

Data Mining:Data mining is a process starting from apparently unstructured data to find interesting patterns.

机Learning:机器学习使用大量的算法。

Data

Data Mining:Data mining is used to extract data from any data warehouse.

机Learning:机learning is to read the machine which relates to system software.

Application

Data Mining:Data mining mainly utilizes data from a particular domain.

机Learning:机learning techniques are fairly generic and can be applied to various settings.

Focus

Data Mining:Data mining community focuses mainly on algorithms and applications.

机Learning:机learning communities pays more on theories.

Methodology

Data Mining:Data mining is used to get rules from data.

机Learning:机learning teaches the computer to learn and understand given rules.

Research

Data Mining:Data mining is a research area that uses methods like machine learning.

机Learning:机learning is a methodology that is used to allow computers to do intelligent tasks.

Summary:

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.

Image Courtesy:

1. "CRISP-DM Process Diagram" by Kenneth Jensen - Own work. [CC BY-SA 3.0] viaWikimedia Commons

2. "Automated online assistant" by Bemidji State University [Public Domain] viaWikimedia Commons