统计、机器学习和数据挖掘

Question

我目前正在学习数据挖掘，有以下问题。

1. 机器学习和数据挖掘之间有什么关系？
2. 我发现许多数据挖掘技术都与统计相关，而我“听说”数据挖掘与机器学习有很多关系。所以我的问题是:机器学习与统计学密切相关吗？
3. 如果它们没有密切相关，是否存在将侧重于统计技术的数据挖掘和侧重于机器学习技能的数据挖掘分开的划分？因为我发现有些研究生院统计系开设了数据挖掘类(class)。

Answer 1

数据挖掘是从数据中提取有用信息的过程，例如模式、趋势、客户/用户行为、喜欢/不喜欢等。这涉及使用与人工智能和统计相关的算法。

Wikipedia数据挖掘的定义是:

Data Mining (the analysis step of the Knowledge Discovery in Databases process,[1] or KDD), a relatively young and interdisciplinary field of computer science,[2][3] is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management. In contrast to for example machine learning, the emphasis lies on the discovery of previously unknown patterns as opposed to generalizing known patterns to new data.

机器学习涉及让计算机“学习”行为、趋势等，并据此采取行动。例如，在信用卡欺诈中，计算机“学习”客户的行为，如果发生奇怪的情况(涉及非常高金额的交易等)，它会将该交易标记为潜在的欺诈。

维基百科对机器学习的定义是:

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Machine Learning is concerned with the development of algorithms allowing the machine to learn via inductive inference based on observing data that represents incomplete information about statistical phenomenon. Classification which is also referred to as pattern recognition, is an important task in Machine Learning, by which machines “learn” to automatically recognize complex patterns, to distinguish between exemplars based on their different patterns, and to make intelligent decisions.

机器学习使用数据挖掘来学习模式、行为、趋势等，因为数据挖掘是从一组数据中提取这些信息的方法。数据挖掘和机器学习都使用统计数据来做出决策。所以，是的，统计数据在数据挖掘和机器学习中涉及并且非常重要。