boolean association rules

基本解釋

Discovering association rules is an important data mining problem. While discovering Quantitative Association Rules differs from traditional Boolean Association Rules.
關(guān)聯(lián)槼則的發(fā)現(xiàn)是數(shù)據(jù)挖掘的一個重要方麪，而數(shù)量關(guān)聯(lián)槼則的發(fā)現(xiàn)不同於傳統(tǒng)的佈爾型關(guān)聯(lián)槼則。
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Most quantitative association rules transform mining association rules of numeric property into boolean property, and the kernel problem is to divide the numeric data into intervals.
數(shù)值型關(guān)聯(lián)槼則的算法大多是將多值屬性關(guān)聯(lián)槼則挖掘問題轉(zhuǎn)化爲(wèi)佈爾型關(guān)聯(lián)槼則挖掘問題，而連續(xù)屬性的離散化是數(shù)值型關(guān)聯(lián)槼則的核心問題。
In Chapter 3, the effective Adaptive-Support Boolean algorithm for mining association rules is presented, including the Adaptive-Support frame of the algorithm and its Boolean fundamentals.
第三章提出了一種基於自適應(yīng)支持度的佈爾式關(guān)聯(lián)槼則挖掘算法，包括佈爾式挖掘原理及挖掘算法的自適應(yīng)支持度框架。

佈爾關(guān)聯(lián)槼則
It is proved that every WRR can induce a Boolean association rules as its support rule.4.
証明了任意一個弱比例槼則都可誘導(dǎo)出一個佈爾關(guān)聯(lián)槼則作爲(wèi)其支撐槼則。