本文關(guān)鍵詞： 信息系統(tǒng) 知識(shí)發(fā)現(xiàn) 粒計(jì)算 規(guī)則提取 真值表　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：信息系統(tǒng)是數(shù)據(jù)的一種重要的表現(xiàn)形式,從信息系統(tǒng)中通過(guò)算法搜索隱藏信息的過(guò)程是知識(shí)發(fā)現(xiàn)的主要內(nèi)容。真值表是一種特殊形式的信息系統(tǒng),在數(shù)字電路的組合邏輯的應(yīng)用中占有重要的地位。粒計(jì)算是近年發(fā)展起來(lái)的用來(lái)解決復(fù)雜問(wèn)題、處理智能信息的一種新的計(jì)算方式。粗糙集是粒計(jì)算中重要的理論工具,可以對(duì)數(shù)據(jù)進(jìn)行分析和推理,從中發(fā)現(xiàn)隱含的知識(shí),揭示潛在的規(guī)律。規(guī)則提取是粗糙集中知識(shí)發(fā)現(xiàn)的重要研究?jī)?nèi)容之一,是一種獲得信息系統(tǒng)隱含知識(shí)的理論方法。本文從研究粒計(jì)算和粗糙集理論出發(fā),研究信息系統(tǒng)的知識(shí)發(fā)現(xiàn),重點(diǎn)討論了現(xiàn)有的規(guī)則提取算法及所存在的缺陷,基于粒計(jì)算提出了新的信息系統(tǒng)的規(guī)則提取算法,并針對(duì)真值表提出了新的并行約簡(jiǎn)算法。具體工作如下:首先,針對(duì)信息系統(tǒng)的主要形式——決策表,利用粒計(jì)算中粒化的思想,從多粒度角度出發(fā),定義判別向量,在由粗到細(xì)的粒度空間下分別對(duì)決策表進(jìn)行分析,根據(jù)得出的判別向量的元素值提取出信息系統(tǒng)中的規(guī)則;而針對(duì)不一致決策表,需要將不一致決策表轉(zhuǎn)換為一致決策表,然后進(jìn)行規(guī)則提取。本文通過(guò)定理證明和實(shí)例分析說(shuō)明了新算法的有效性,并用UCI數(shù)據(jù)集與現(xiàn)有的規(guī)則提取算法進(jìn)行了對(duì)比試驗(yàn),實(shí)驗(yàn)結(jié)果顯示了新算法的有效性和快速性。然后,針對(duì)信息系統(tǒng)的特殊形式——真值表,首先分析了傳統(tǒng)約簡(jiǎn)算法所存在的缺陷,并基于粒計(jì)算知識(shí)定義了判別矩陣,在多粒度空間下,根據(jù)得出的判別矩陣的元素值提取每個(gè)輸出的最簡(jiǎn)規(guī)則,實(shí)現(xiàn)了真值表的約簡(jiǎn),并通過(guò)并行計(jì)算加快了算法的效率。本文以發(fā)光二極管的真值表為例,闡述了新算法計(jì)算的具體過(guò)程,并比較了公式法、卡諾圖法、Q-M算法等傳統(tǒng)的真值表約簡(jiǎn)算法,通過(guò)數(shù)據(jù)集的測(cè)試表明新算法具有準(zhǔn)確性和快速性。最后,在本文的基礎(chǔ)上設(shè)計(jì)了一個(gè)簡(jiǎn)易的信息系統(tǒng)知識(shí)發(fā)現(xiàn)系統(tǒng),該系統(tǒng)集成了現(xiàn)有的一些決策表規(guī)則提取算法,并且針對(duì)真值表設(shè)計(jì)了一個(gè)對(duì)真值表進(jìn)行約簡(jiǎn)的子系統(tǒng),便于用戶操作。本文提出的3種信息系統(tǒng)知識(shí)發(fā)現(xiàn)算法,克服了現(xiàn)有算法的一些弊端,通過(guò)算法得到的決策規(guī)則在準(zhǔn)確性和簡(jiǎn)易性方面得到了提升,實(shí)現(xiàn)了數(shù)據(jù)的快速規(guī)則提取過(guò)程。
[Abstract]:Information system is an important representation of data. The process of searching and hiding information through algorithm in information system is the main content of knowledge discovery. Granular computing is a new computing method developed in recent years to solve complex problems and deal with intelligent information. Rough set is an important theoretical tool in granular computing. The data can be analyzed and inferred, and hidden knowledge can be found from it. Rule extraction is one of the important research contents of knowledge discovery in rough sets. This paper studies the knowledge discovery of information system from the perspective of granular computing and rough set theory, and discusses the existing rules extraction algorithms and their defects. A new rule extraction algorithm for information system based on granular computing is proposed, and a new parallel reduction algorithm for truth table is proposed. The main work is as follows: firstly, the decision table, the main form of information system, is proposed. The discriminant vector is defined from the point of view of multi-granularity, and the decision table is analyzed separately in the coarse to fine granularity space, and the rules in the information system are extracted according to the element value of the discriminant vector. For the inconsistent decision table, it is necessary to convert the inconsistent decision table into the consistent decision table, and then to extract the rules. This paper proves the validity of the new algorithm by theorem proof and example analysis. The UCI data set is compared with the existing rule extraction algorithm. The experimental results show the effectiveness and rapidity of the new algorithm. Then, aiming at the special form of information system-truth table, Firstly, the defects of the traditional reduction algorithm are analyzed, and the discriminant matrix is defined based on the granular computing knowledge. In the multi-granularity space, the minimum rule of each output is extracted according to the element value of the discriminant matrix, and the reduction of the truth table is realized. The efficiency of the algorithm is accelerated by parallel computation. Taking the truth table of LED as an example, this paper expounds the concrete process of the new algorithm, and compares the traditional truth-table reduction algorithms such as formula method, Carnot diagram method and Q-M algorithm. The test of data sets shows that the new algorithm is accurate and fast. Finally, a simple information system knowledge discovery system is designed based on this paper. The system integrates some existing decision table rule extraction algorithms. A sub-system is designed to reduce the truth table, which is easy for users to operate. The three knowledge discovery algorithms of information system proposed in this paper overcomes some disadvantages of the existing algorithms. The decision rules obtained by the algorithm are improved in accuracy and simplicity, and the fast rule extraction process is realized.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN79;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王倩倩;胡蓉;周志杰;錢(qián)斌;;基于置信規(guī)則庫(kù)的水松紙透氣度在線檢測(cè)研究[J];控制工程;2016年09期

2 劉文玲;錢(qián)曉飛;裴軍;;基于關(guān)聯(lián)規(guī)則的公交事故受傷情況預(yù)測(cè)研究[J];控制工程;2016年09期

3 常玉慧;郭慶軍;錢(qián)進(jìn);;不一致決策表快速知識(shí)約簡(jiǎn)算法研究[J];小型微型計(jì)算機(jī)系統(tǒng);2015年08期

4 陳澤華;馬賀;;基于粒矩陣的多輸入多輸出真值表快速并行約簡(jiǎn)算法[J];電子與信息學(xué)報(bào);2015年05期

5 陳澤華;張?jiān)?謝剛;;不一致決策表規(guī)則獲取的粒計(jì)算方法[J];控制與決策;2015年04期

6 張明;程科;楊習(xí)貝;唐振民;;基于加權(quán)粒度的多粒度粗糙集[J];控制與決策;2015年02期

7 徐計(jì);王國(guó)胤;于洪;;基于粒計(jì)算的大數(shù)據(jù)處理[J];計(jì)算機(jī)學(xué)報(bào);2015年08期

8 陳澤華;張?jiān)?謝剛;;基于粒計(jì)算的最簡(jiǎn)決策規(guī)則挖掘算法[J];控制與決策;2015年01期

9 江峰;王莎莎;杜軍威;眭躍飛;;基于近似決策熵的屬性約簡(jiǎn)[J];控制與決策;2015年01期

10 馬希驁;王國(guó)胤;于洪;;決策域分布保持的啟發(fā)式屬性約簡(jiǎn)方法[J];軟件學(xué)報(bào);2014年08期

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