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首页> 外文期刊>International Journal of Soft Computing and Software Engineering >Efficient Knowledge Discovery for an Intelligent Dataminer
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Efficient Knowledge Discovery for an Intelligent Dataminer

机译：智能数据挖掘者的高效知识发现

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In every field, data is the most significant property, When the data concealed in the rare data it is exposed. Processing of non-trivial removal of novel, which actionable and implicit information from the huge data is Datamining which is an investigation of data, difficult to find out the most helpful patterns and information, which are not understandable to the data user. It computes the patterns and relationships in a rare data and distributes the outcome which can be either assessed by a human analyst or make use of in automatic decision support system. Multiple stages are available in the Data mining. In data mining very simple process are involved, they are post processing the mined result, pre-processing the data and choosing the suitable mining algorithm. At every stage, these are more option that is probable. With this data mining process, the proposed system is operated by selecting the suitable Data mining algorithm for the user’s needs. Proposed system first performs the pre-processing the data, which converts the data into most appropriate form for use by selecting the algorithm. Pre-processed mined data is post-processed and acquire a pattern as command through the user. The main aim of this project is solved by deploying the developed pattern. This proposed system general structure. This general structure is used for any type of data set and can used as make possible human analysis is the one of the particular tool. This tool mostly used to create intelligence huge quantity of data, which need processing to create knowledgeable conclusion. The conclusion is created by the back propagation method in neural network. At last, propose system the performance and evolution is shown, which clearly explains the proposed system have best tool to choose the appropriate algorithm in data mining.
机译：在每个字段中，数据都是最重要的属性，当数据隐藏在稀有数据中时，就会暴露出来。不费吹灰之力地从海量数据中删除新颖的，可操作的隐式信息的过程是Datamining，它是对数据的调查，难以找出数据用户无法理解的最有用的模式和信息。它计算稀有数据中的模式和关系，并分配结果，该结果可以由人工分析人员评估，也可以在自动决策支持系统中使用。数据挖掘可分为多个阶段。在数据挖掘中，涉及到非常简单的过程，它们是对挖掘的结果进行后处理，对数据进行预处理并选择合适的挖掘算法。在每个阶段，这些都是可能的更多选择。通过这种数据挖掘过程，可以通过选择适合用户需求的数据挖掘算法来运行建议的系统。拟议的系统首先执行数据预处理，然后通过选择算法将数据转换为最适合的形式以供使用。预处理后的挖掘数据将进行后处理，并通过用户作为命令获取模式。通过部署开发的模式可以解决该项目的主要目标。本文提出了系统的总体结构。这种通用结构可用于任何类型的数据集，并且可以使可能的人工分析成为特定工具之一。该工具主要用于创建大量数据的智能，需要进行处理才能创建知识丰富的结论。结论是通过神经网络中的反向传播方法得出的。最后，给出了所提出的系统的性能和演进过程，清楚地说明了所提出的系统具有在数据挖掘中选择合适算法的最佳工具。

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