THE KL-MINER PROCEDURE FOR DATAMINING

Vaclav Lin; Petr Dolejsi; Jan Rauch; Milan Simunek

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THE KL-MINER PROCEDURE FOR DATAMINING

机译：KL-MINER数据挖掘程序

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摘要

KL-Miner is a datamining procedure that, given input data matrix M and a set of parameters, generates patterns of the form R ～ C/γ. Here R and C are categorial attributes corresponding to the columns of M, and γ is a Boolean condition defined in terms of the remaining colums of M The pattern R ～ C/γ means that R and C are strongly correlated on the submatrix of M formed by all the rows of M that satisfy γ. What is meant by "strong correlation" and how are R, C and γ generated is determined by the input parameters of the procedure. KL-Miner conforms to the GUHA principle formulated in. It revives two older GUHA procedures described in: it is very much related to CORREL and contains a new implementation of COLLAPS as a module. In this paper, we mention the motivation that leads to designing of KL-Miner, describing our new implementation of COLLAPS and giving application examples that illustrate the main features of KL-Miner.

机译：KL-Miner是一种数据挖掘程序，在给定输入数据矩阵M和一组参数的情况下，它生成R〜C /γ形式的模式。这里的R和C是与M的列相对应的类别属性，并且γ是根据M的其余列定义的布尔条件。模式R〜C /γ表示R和C与形成的M的子矩阵高度相关满足γ的所有M行。 “强相关性”的含义以及R，C和γ的生成方式取决于该过程的输入参数。 KL-Miner遵循其中制定的GUHA原则。它复兴了以下描述的两个旧的GUHA程序：它与CORREL密切相关，并包含一个新的COLLAPS实现模块。在本文中，我们提到了导致KL-Miner设计的动机，描述了COLLAPS的新实现，并给出了说明KL-Miner主要功能的应用示例。

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来源
《Neural Network World》 |2004年第5期|p.411-420|共10页
作者
Vaclav Lin; Petr Dolejsi; Jan Rauch; Milan Simunek;
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作者单位

Department of Information and Knowledge Engineering;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
data mining; contingency tables; GUHA;

机译：数据挖掘;列联表;GUHA;

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THE KL-MINER PROCEDURE FOR DATAMINING

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