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在学术界，主要的研究论坛始于1995年，当时在 AAAI 的赞助下在蒙特利尔举行了第一次数据挖掘和知识发现国际会议(KDD-95)。会议由乌萨马 · 法耶兹和拉马萨米 · 乌图鲁萨米共同主持。一年后，也就是1996年，乌萨马 · 法耶兹(Usama Fayyad)创办了这本由克卢维尔(Kluwer)撰写的期刊，并将其命名为《数据挖掘与知识发现》(Data Mining and Knowledge Discovery) ，担任创刊主编。后来他创办了 SIGKDD 时事通讯 SIGKDD Explorations。Kdd 国际会议成为数据挖掘领域最高质量的会议，研究论文的接受率低于18% 。《数据挖掘与知识发现》是该领域的主要研究期刊。

在学术界，主要的研究论坛始于1995年，当时在 AAAI 的赞助下在蒙特利尔举行了第一次数据挖掘和知识发现国际会议(KDD-95)。会议由乌萨马 · 法耶兹和拉马萨米 · 乌图鲁萨米共同主持。一年后，也就是1996年，乌萨马 · 法耶兹(Usama Fayyad)创办了这本由克卢维尔(Kluwer)撰写的期刊，并将其命名为《数据挖掘与知识发现》(Data Mining and Knowledge Discovery) ，担任创刊主编。后来他创办了 SIGKDD 时事通讯 SIGKDD Explorations。Kdd 国际会议成为数据挖掘领域最高质量的会议，研究论文的接受率低于18% 。《数据挖掘与知识发现》是该领域的主要研究期刊。

==Background==

==背景 Background==

The manual extraction of patterns from [[data]] has occurred for centuries. Early methods of identifying patterns in data include [[Bayes' theorem]] (1700s) and [[regression analysis]] (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As [[data set]]s have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as [[neural networks]], [[cluster analysis]], [[genetic algorithms]] (1950s), [[decision tree learning|decision trees]] and [[decision rules]] (1960s), and [[support vector machines]] (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns<ref name="Kantardzic">{{cite book |last=Kantardzic |first=Mehmed |title=Data Mining: Concepts, Models, Methods, and Algorithms |year=2003 |publisher=John Wiley & Sons |isbn=978-0-471-22852-3 |oclc=50055336 |url-access=registration |url=https://archive.org/details/dataminingconcep0000kant }}</ref> in large data sets. It bridges the gap from [[applied statistics]] and artificial intelligence (which usually provide the mathematical background) to [[database management]] by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

The manual extraction of patterns from [[data]] has occurred for centuries. Early methods of identifying patterns in data include [[Bayes' theorem]] (1700s) and [[regression analysis]] (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As [[data set]]s have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as [[neural networks]], [[cluster analysis]], [[genetic algorithms]] (1950s), [[decision tree learning|decision trees]] and [[decision rules]] (1960s), and [[support vector machines]] (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns<ref name="Kantardzic">{{cite book |last=Kantardzic |first=Mehmed |title=Data Mining: Concepts, Models, Methods, and Algorithms |year=2003 |publisher=John Wiley & Sons |isbn=978-0-471-22852-3 |oclc=50055336 |url-access=registration |url=https://archive.org/details/dataminingconcep0000kant }}</ref> in large data sets. It bridges the gap from [[applied statistics]] and artificial intelligence (which usually provide the mathematical background) to [[database management]] by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

从数据中手工提取图案已经发生了几个世纪了。早期识别数据中模式的方法包括贝叶斯定理(17世纪)和回归分析定理(19世纪)。计算机技术的扩散、普及和不断增强的能力极大地提高了数据的收集、存储和操作能力。随着数据集的规模和复杂性的增长，直接的“实际操作”数据分析越来越多地借助于间接的、自动化的数据处理，辅之以计算机科学领域的其他发现，特别是在机器学习领域，如神经网络、数据聚类、遗传算法(1950年代)、决策树和决策规则(1960年代) ，以及支持向量机(1990年代)。数据挖掘就是应用这些方法来发现大型数据集中的隐藏模式的过程。它利用数据在数据库中存储和索引的方式，更有效地执行实际的学习和发现算法，从而弥补了从应用统计学和人工智能(通常提供数学背景)到数据库管理之间的差距，使这些方法能够应用于更大的数据集。

从数据中手工提取图案已经发生了几个世纪了。早期识别数据中模式的方法包括贝叶斯定理(17世纪)和回归分析定理(19世纪)。计算机技术的扩散、普及和不断增强的能力极大地提高了数据的收集、存储和操作能力。随着数据集的规模和复杂性的增长，直接的“实际操作”数据分析越来越多地借助于间接的、自动化的数据处理，辅之以计算机科学领域的其他发现，特别是在机器学习领域，如神经网络、数据聚类、遗传算法(1950年代)、决策树和决策规则(1960年代) ，以及支持向量机(1990年代)。数据挖掘就是应用这些方法来发现大型数据集中的隐藏模式的过程。它利用数据在数据库中存储和索引的方式，更有效地执行实际的学习和发现算法，从而弥补了从应用统计学和人工智能(通常提供数学背景)到数据库管理之间的差距，使这些方法能够应用于更大的数据集。

==Process==

==Process==