【摘要】：針對(duì)經(jīng)典概念格基于完備形式背景,在缺值形式背景下不能很好地獲取關(guān)聯(lián)規(guī)則知識(shí)的問(wèn)題,提出了一種τ-近似概念格來(lái)解決信息不完備背景下的知識(shí)發(fā)現(xiàn)問(wèn)題。τ-近似概念格在偏小近似形式背景的基礎(chǔ)上,基于區(qū)間概念格思想構(gòu)建,對(duì)τ-近似概念格的構(gòu)建及其關(guān)聯(lián)規(guī)則提取過(guò)程進(jìn)行了詳細(xì)描述,并獲取到了精度和可信度較高的關(guān)聯(lián)規(guī)則知識(shí)。通過(guò)將其與偏小近似概念格和偏大近似概念格這兩種策略進(jìn)行案例分析對(duì)比,驗(yàn)證了它的可行性和有效性。
[Abstract]:In view of the problem that classical concept lattice is based on complete formal background and can not get the knowledge of association rules well under the background of missing form, A 蟿-approximation concept, Ge Lai, is proposed to solve the problem of knowledge discovery under the background of incomplete information. The 蟿-approximation concept lattice is constructed on the basis of the partial approximate formal background and the idea of interval concept lattice, and the concept lattice of 蟿-approximation is constructed based on the idea of interval concept lattice. The construction of 蟿-approximate concept lattice and the process of extracting association rules are described in detail, and the knowledge of association rules with high accuracy and credibility is obtained. The feasibility and effectiveness of the proposed strategy are verified by case analysis and comparison of the two strategies: the partial approximation concept lattice and the partial approximation concept lattice.
【作者單位】： 華北理工大學(xué)信息工程學(xué)院;
【基金】：河北省科技支撐計(jì)劃資助項(xiàng)目(15210110D)
【分類號(hào)】：TP311.13

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