数据挖掘注重对海量数据中有效规则的提炼。机器学习注重用大量数据训练出来的有效模式。利用的算法规则大体相似。
本文主要分为两部分,第一部分阐述数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)之间的区别。这三者的区别主要是目的不同,其手段(算法,模型)有很大的重叠,所以容易混淆。第二部分主要阐述以上的技能与数据科学(data science)的关系,以及数据科学(data science)和商业分析(business analytics)之间的...
http://upfrontanalytics.com/data-mining-vs-artificial-intelligence-vs-machine-learning/ https://www.researchgate.net/post/What_is_the_difference_between_machine_learning_and_data_mining https://www.r-bloggers.com/whats-the-difference-between-machine-learning-statistics-and-data-mining/ https://disc...
Data mining属于跨学科领域， 主要是研究和发现数据的各种属性。进行这项研究的手段有很多种， machine l...
Data mining and machine learning are related fields, but they have different purposes:The goal of machine learning is to develop algorithms that allow computers to learn without human intervention. It’s about making machines smarter, so they can carry out tasks related to human intelligence ...
推断统计学侧重于通过样本数据推断总体。“Data Mining”在通过算法的得到的结果上,采用描述性统计学(大部分)来解释问题。而”Machine Learning”则是侧重于通过推断统计学来实现的自学习。 这也正是在MLAPP(Machine Learning:A Probabilistic Perspective)书上说的一句话“in data mining, there is more emphasis on...
Data mining and Machine Learning fall under the same world of Science. Though these terms are confused with each other, there are some major differences between them. #1) Scope:Data Mining is used to find out how different attributes of a data set are related to each other through patterns ...
However, machine learning only uses algorithms and possesses a self-learning capability to change rules per scenario to find the solution. Another contrasting difference lies in the human effort, as data mining requires constant human intervention but machine learning only requires humans to define the...
What is the difference between data mining and machine learning? http://www.google.com.hk/search?hl=zh-TW&client=firefox-a&hs=K3s&rls=org.mozilla%3Azh-TW%3Aofficial&q=difference+between+data+mining+and+machine+learning&aq=f&aqi=g1&aql=&oq=...
Machine Learning and Data Mining Machine learning and data mining are two of the most important fields in computer science today. With the increasing amount of data being generated every day, it has become essential to develop tools and techniques that can help us extract meaningful insights from...