数据挖掘技术系统综述

N. Busis
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引用次数: 0

摘要

信息挖掘是在庞大而复杂的信息指标中揭示计划和模型。信息挖掘有两种类型:模型构建和模型区域。数据挖掘中的模型设计实际上类似于可验证的说明,但由于数据记录的巨大规模以及数据挖掘习惯上是任意数据评估的方式,产生了新的问题。模型披露搜索数据中的怪癖或最小的接近计划,而大量的数据是微不足道的。毫无疑问,对各种大规模数据挖掘实践的一种观点是,它们本质上包含隔离和信息减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic Survey of Data Mining Technique
Information mining is the disclosure of plans and models in huge and complex informational indexes. There are two sorts of information mining: Model construction and model area. Model design in data mining is actually similar to verifiable illustrating, but new issues arise by virtue of the huge sizes of the data records and the way that Data mining is habitually discretionary data assessment. Model disclosure searches for quirks or minimal close by plans in data, with the enormous mass of the data being insignificant. Without a doubt, one viewpoint on various large-scale data mining practices is that they essentially contain isolating and information decrease.
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