基于粒計算和粗糙集的聚類算法研究
發(fā)布時間：2018-10-13 11:43
【摘要】：全球信息科技和互聯(lián)網(wǎng)絡的迅速發(fā)展,使得人們對各種網(wǎng)絡資源共享的需求越來越大,這些共享的數(shù)據(jù)信息造成了數(shù)據(jù)膨脹和信息爆炸。如何找到一種科學合理的方式來幫助人們從大量紛雜無章的數(shù)據(jù)中篩選出有效可靠的信息是急需研究的問題。數(shù)據(jù)挖掘是解決該問題的一種有效方法,它可以幫助人們在對特定的數(shù)據(jù)進行專業(yè)的處理之后做出正確高效的決策。聚類本身屬于數(shù)據(jù)挖掘里面的關鍵內(nèi)容,故成為許多專家學者的研究對象。在經(jīng)典的聚類方法基礎上,本文分析了聚類算法的局限性,然后研究了蜂群算法、粒子群算法、粗糙集以及粒計算的理論知識,之后結(jié)合人工蜂群、粒子群、粗糙集和粒計算來對傳統(tǒng)聚類算法進行優(yōu)化。主要工作如下:(1)經(jīng)典的K-medoids聚類算法具有起始類中心隨機獲取、準確率不夠高、全局尋優(yōu)時表現(xiàn)不佳的缺陷,為此,提出了一種基于人工蜂群的優(yōu)化聚類算法。該算法結(jié)合改進粒計算和最大距離積法選取初始聚類中心,然后動態(tài)調(diào)整搜索步長,采用基于排序的選擇概率來實現(xiàn)跟隨蜂對引領蜂的選取,增加了算法完成最終尋優(yōu)的速度,降低了早熟收斂情況發(fā)生的概率。實驗結(jié)果表明:該算法降低了對起始中心分布的敏感程度,且準確率和穩(wěn)定性都得到較大提升。(2)K-means聚類方法具有對起始類中心依靠性大、無法處理邊界對象、精度不夠高和穩(wěn)定性差等缺陷,本文將粒子群與粗糙集進行融合后再用于聚類問題中。該算法初始化采用密度和最大距離積法,并使慣性權(quán)重的取值用線性遞減和隨機分布的方法來實現(xiàn),然后調(diào)整學習因子、引入隨機粒子,增加種群的多樣性。最后將改進后的算法與粒子群和粗糙集結(jié)合,并用之來優(yōu)化K-means。實驗結(jié)果表明:該算法在一定程度上弱化了對原始聚類中心的依賴,能有效地整理邊界數(shù)據(jù),準確率和穩(wěn)定性也得到了改善。
[Abstract]:With the rapid development of global information technology and Internet, the demand for the sharing of various network resources is increasing, and these shared data information cause data expansion and information explosion. How to find a scientific and reasonable way to help people to screen out effective and reliable information from a large number of complicated data is an urgent problem. Data mining is an effective method to solve this problem. It can help people make correct and efficient decision after dealing with specific data professionally. Clustering itself belongs to the key content of data mining, so it becomes the research object of many experts and scholars. Based on the classical clustering method, this paper analyzes the limitations of the clustering algorithm, and then studies the theoretical knowledge of bee swarm algorithm, particle swarm algorithm, rough set and particle computing. The traditional clustering algorithm is optimized by rough set and granular computing. The main work is as follows: (1) the classical K-medoids clustering algorithm has the shortcomings of random acquisition of the starting cluster center, low accuracy and poor global optimization. Therefore, an artificial swarm based optimization clustering algorithm is proposed. The algorithm combines improved particle computation and maximum distance product method to select the initial cluster center, then dynamically adjusts the search step size, and adopts the selection probability based on sorting to select the following bee to lead bee, which increases the speed of the algorithm to complete the final optimization. The probability of premature convergence is reduced. The experimental results show that the algorithm reduces the sensitivity to the initial center distribution, and the accuracy and stability are greatly improved. (2) the K-means clustering method is highly dependent on the center of the starting class and can not handle the boundary object. Because the precision is not high and the stability is poor, the particle swarm and rough set are fused and then applied to the clustering problem. Density and maximum distance product are used to initialize the algorithm and the method of linear decrement and random distribution is used to determine the inertial weight. Then the learning factor is adjusted and the random particle is introduced to increase the diversity of the population. Finally, the improved algorithm is combined with particle swarm optimization and rough set to optimize K-means. The experimental results show that the algorithm weakens the dependence on the original clustering center to a certain extent and can effectively collate the boundary data. The accuracy and stability of the algorithm are also improved.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP18;TP311.13
本文編號：2268494
[Abstract]:With the rapid development of global information technology and Internet, the demand for the sharing of various network resources is increasing, and these shared data information cause data expansion and information explosion. How to find a scientific and reasonable way to help people to screen out effective and reliable information from a large number of complicated data is an urgent problem. Data mining is an effective method to solve this problem. It can help people make correct and efficient decision after dealing with specific data professionally. Clustering itself belongs to the key content of data mining, so it becomes the research object of many experts and scholars. Based on the classical clustering method, this paper analyzes the limitations of the clustering algorithm, and then studies the theoretical knowledge of bee swarm algorithm, particle swarm algorithm, rough set and particle computing. The traditional clustering algorithm is optimized by rough set and granular computing. The main work is as follows: (1) the classical K-medoids clustering algorithm has the shortcomings of random acquisition of the starting cluster center, low accuracy and poor global optimization. Therefore, an artificial swarm based optimization clustering algorithm is proposed. The algorithm combines improved particle computation and maximum distance product method to select the initial cluster center, then dynamically adjusts the search step size, and adopts the selection probability based on sorting to select the following bee to lead bee, which increases the speed of the algorithm to complete the final optimization. The probability of premature convergence is reduced. The experimental results show that the algorithm reduces the sensitivity to the initial center distribution, and the accuracy and stability are greatly improved. (2) the K-means clustering method is highly dependent on the center of the starting class and can not handle the boundary object. Because the precision is not high and the stability is poor, the particle swarm and rough set are fused and then applied to the clustering problem. Density and maximum distance product are used to initialize the algorithm and the method of linear decrement and random distribution is used to determine the inertial weight. Then the learning factor is adjusted and the random particle is introduced to increase the diversity of the population. Finally, the improved algorithm is combined with particle swarm optimization and rough set to optimize K-means. The experimental results show that the algorithm weakens the dependence on the original clustering center to a certain extent and can effectively collate the boundary data. The accuracy and stability of the algorithm are also improved.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP18;TP311.13
【參考文獻】
相關期刊論文 前10條
1 夏卓群;歐慧;李平;武志偉;戴傲;;基于改進流形距離和人工蜂群的二階段聚類算法[J];控制與決策;2016年03期
2 朱蓉;靳雁霞;范衛(wèi)華;;融合優(yōu)質(zhì)粒子分布的粒子群優(yōu)化算法[J];小型微型計算機系統(tǒng);2015年03期
3 羅可;李蓮;周博翔;;一種蜜蜂交配優(yōu)化聚類算法[J];電子學報;2014年12期
4 袁周米琪;周堅華;;自適應確定K-means算法的聚類數(shù):以遙感圖像聚類為例[J];華東師范大學學報(自然科學版);2014年06期
5 沈艷;余冬華;王昊雷;;粒子群K-means聚類算法的改進[J];計算機工程與應用;2014年21期
6 熊眾望;羅可;;基于改進的簡化粒子群聚類算法[J];計算機應用研究;2014年12期
7 劉露;彭濤;左萬利;戴耀康;;一種基于聚類的PU主動文本分類方法[J];軟件學報;2013年11期
8 黃月;吳成東;張云洲;程龍;孫堯;;基于K均值聚類的二進制傳感器網(wǎng)絡多目標定位方法[J];控制與決策;2013年10期
9 段其昌;唐若笠;徐宏英;李文;;粒子群優(yōu)化魚群算法仿真分析[J];控制與決策;2013年09期
10 趙志剛;黃樹運;王偉倩;;基于隨機慣性權(quán)重的簡化粒子群優(yōu)化算法[J];計算機應用研究;2014年02期
相關博士學位論文 前2條
1 張靜;基于粗糙集理論的數(shù)據(jù)挖掘算法研究[D];西北工業(yè)大學;2006年
2 馬昕;粗糙集理論在數(shù)據(jù)挖掘領域中的應用[D];浙江大學;2003年
相關碩士學位論文 前1條
1 黃雯;數(shù)據(jù)挖掘算法及其應用研究[D];南京郵電大學;2013年
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