人工智能，机器学习，统计学，数据挖掘之间有什么区别？ - 推酷
人工智能，机器学习，统计学，数据挖掘之间有什么区别？
Few day ago before I saw an interesting question on
that got my attention for a while. After spending few minutes of readings and analyzing all answers on stack I felt writing my thoughts assuming what I would have answered if I really had too.
What is the difference between Artificial Intelligence, Machine Learning, Statistics, and Data Mining ?
Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind of hierarchy between them, what would it be?
I assume the author of that question is trying to get a clear picture by understanding the line of separation that distinguish each field from the other. So here is my take to explain it in a more simplified way that I ever could do.
Machine learning is a science that involves development of self-learning algorithms. These algorithms are more generic in nature that it can be applied to various domain related problems.
Data mining is a practice of applying algorithms (mostly Machine learning algorithms) with the data available from domain to solve domain related problems.
Statistics is a study of how to collect, organizes, analyze, and interpret numerical information from data. Statistics can slip into two taxonomy called Descriptive statistics and Inferential statistics. Descriptive statistics involves method of organizing, summering and picturing information from data. Inferential statistics invokes method of using information from sample to draw conclusion about the population.
Machine learning uses statistics (mostly inferential statistics) to develop self learning algorithms.
Data mining uses statistics (mostly Descriptive statistics) on results obtained from algorithms, it used to solve the problem.
Data mining as a field emerged to solve problems in the miscellaneous domain (particularity in business), acquired different techniques and practices that are used in different field of studies.
In 1960 practitioners who solved problems (mostly business problems) used term Data fishing to call the work they do. In 1989 Gregory Piatetsky Shapiro used term knowledge Discovery in the Database (KDD). In 1990 a company used term Data mining with the trademark to represent their work. Today data mining and KDD are used interchangeably.
Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumference. As field with extremely broad scope, AI has defined its goal into multiple chunks. Later each chuck has become a separate field of study to solve its problem.
Here is a major list of AI goal (a.k.a. AI problems)
1. Reasoning
2. Knowledge representation
3. Automated planning and scheduling
4. Machine learning
5. Natural language processing
6. Computer vision
7. Robotics
8. General intelligence, or strong AI
As mentioned in the list Machine learning is field emerged from one the AI goal to help machine or software to learn on it own to solve problems it’s can come across.
Natural language processing is another such field emerged from AI goal to help machine to communicate with real human.
Computer vision is a field emerged from AI goal to identify and distinguish objects that the machine could see.
Robotics is a field emerged from AI goal to give a physical appearance for a machine to do physical actions.
Is some kind of hierarchy between them, what would it be?
One way of representing the hierarchical relationship between these science and study can be drawn from historical facts when they have emerged.
Origin of science and study.
Statistics – 1749
Artificial Intelligence – 1940
Machine leaning – 1946
Data mining – 1980
History of statistics is believed to be started around 1749 to represent information. Practitioners use statistics to represent the economic status of states and to represent the material resource put on the military use. Later usage of statistics was leveraged to include data analysis and organization.
History of Artificial Intelligence happened to be existing has two types namely classic and modern. Classical Artificial Intelligence can be seen in ancient time stories and writings. However Modern AI emerged in 1940 when describing the idea of mimicking human like machine.
In 1946, Origin of Machine leaning emerged as branch of Artificial Intelligence with purpose to solve the goal of making machines to learning itself without programming/ hardwiring explicitly.
Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches?
It would be appropriate to say they (Statistics, Artificial Intelligence and Machine Leaning) are highly inter dependent field that they can’t survive along without leading help from others. It is also good to see these 3 fields a one globe field instead of 3 diffident subjects.
As with this perception as one globe field these three fields have contributed their excellence in solving common goal. As a result the solution as such where applicable in many different domains where the core problem is same under the hood.
This is time data mining come into picture, it took the solution obtained from the globe field and applied it to different domains(business, military, medicine, space) to solve problems that has the same nature under the hood. This is also the time where data mining expanded its popularity.
I Hope my explanation has everything that need to answer the authors question and I believed it would have definitely helped anyone who is trying to understand the sweet spot of each field and how they are related. If you got anything to say or share about the article then please let me know your thoughts in the comment section.
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这学期分别学习了《数据挖掘》《机器学习》和《模式识别》三门课程，为了搞明白这三者的关系，就google了下，一下为一些从网上获得的资料。
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数据挖掘和机器学习的区别和联系，周志华有一篇很好的论述《机器学习与数据挖掘》可以帮助大家理解。数据挖掘受到很多学科领域的影响，其中数据库、机器学习、统计学无疑影响最大。简言之，对数据挖掘而言，数据库提供数据管理技术，机器学习和统计学提供数据分析技术......
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大家来说说数据挖掘、人工智能和机器学习三者的联系和区别是什么？
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软件下载指南数据挖掘与机器学习的区别-技术方案-@大数据资讯
你好，游客
数据挖掘与机器学习的区别
来源：CSDN博客&
作者：Diehard_Yin
　　数据挖掘和机器学习的区别和联系，周志华有一篇很好的论述《机器学习和数据挖掘》可以帮助大家理解。
　　数据挖掘受到很多学科领域的影响，其中数据库、机器学习、统计学无疑影响最大。简言之，对数据挖掘而言，数据库提供数据管理技术，机器学习和统计学提供数据分析技术。
　　由于统计学往往醉心于理论的优美而忽视实际的效用，因此，统计学界提供的很多技术通常都要在机器学习界进一步研究，变成有效的机器学习算法之后才能再进入数据挖掘领域。从这个意义上说，统计学主要是通过机器学习来对数据挖掘发挥影响，而机器学习和数据库则是数据挖掘的两大支撑技术。
　　从数据分析的角度来看，绝大多数数据挖掘技术都来自机器学习领域，但机器学习研究往往并不把海量数据作为处理对象，因此，数据挖掘要对算法进行改造，使得算法性能和空间占用达到实用的地步。同时，数据挖掘还有自身独特的内容，即关联分析。
　　而模式识别和机器学习的关系是什么呢，传统的模式识别的方法一般分为两种：统计方法和句法方法。句法分析一般是不可学习的，而统计分析则是发展了不少机器学习的方法。也就是说，机器学习同样是给模式识别提供了数据分析技术。
　　至于，数据挖掘和模式识别，那么从其概念上来区分吧，数据挖掘重在发现知识，模式识别重在认识事物。
　　机器学习的目的是建模隐藏的数据结构，然后做识别、预测、分类等。因此，机器学习是方法，模式识别是目的。
　　总结一下吧。只要跟决策有关系的都能叫 AI(人工智能)，所以说 PR(模式识别)、DM(数据挖掘)、IR(信息检索) 属于 AI 的具 体应用应该没有问题。 研究的东西则不太一样， ML(机器学习) 强调自我完善的过程。 Anyway，这些学科都是相通的。
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