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Research On Large outliers in the data set data mining algorithm

机译:数据集数据挖掘算法中的大异常值研究

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Main purpose of outliers mining is from a large number of, incomplete, there are all kinds of data, the found hidden in one of the people is not known in advance but potentially valuable information or knowledge. Outlier is a data: deviate significantly from other data, it does not meet the general patterns or behavior. Outlier data mining has been widely used in the stock market, telecommunications, financial services, intrusion detection, weather forecasting and many other fields. Outliers may be "noise", but it may also be significant events. In practice, in some applications, those rare events are likely to have more value than events that occur frequently. Unfortunately, the outlier data mining is a very important and meaningful work.
机译:异常值挖掘的主要目的是从大量,不完整的,有各种数据,发现隐藏在其中一个人中未提前知道,但潜在的有价值的信息或知识。异常值是一个数据:从其他数据中显着偏离,它不符合常规模式或行为。异常值数据挖掘已广泛用于股票市场,电信,金融服务,入侵检测,天气预报和许多其他领域。异常值可能是“噪声”,但它也可能是重要的事件。在实践中,在某些应用中,那些罕见的事件可能比经常发生的事件更具价值。不幸的是,异常值数据挖掘是一个非常重要和有意义的工作。

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