【摘要】：全球信息科技和互聯(lián)網(wǎng)絡(luò)的迅速發(fā)展,使得人們對(duì)各種網(wǎng)絡(luò)資源共享的需求越來(lái)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(xué)合理的方式來(lái)幫助人們從大量紛雜無(wú)章的數(shù)據(jù)中篩選出有效可靠的信息是急需研究的問(wèn)題。數(shù)據(jù)挖掘是解決該問(wèn)題的一種有效方法,它可以幫助人們?cè)趯?duì)特定的數(shù)據(jù)進(jìn)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(guān)鍵內(nèi)容,故成為許多專家學(xué)者的研究對(duì)象。在經(jīng)典的聚類方法基礎(chǔ)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(jì)算的理論知識(shí),之后結(jié)合人工蜂群、粒子群、粗糙集和粒計(jì)算來(lái)對(duì)傳統(tǒng)聚類算法進(jìn)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(jī)獲取、準(zhǔn)確率不夠高、全局尋優(yōu)時(shí)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(jìn)粒計(jì)算和最大距離積法選取初始聚類中心,然后動(dòng)態(tài)調(diào)整搜索步長(zhǎng),采用基于排序的選擇概率來(lái)實(shí)現(xiàn)跟隨蜂對(duì)引領(lǐng)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(shí)驗(yàn)結(jié)果表明:該算法降低了對(duì)起始中心分布的敏感程度,且準(zhǔn)確率和穩(wěn)定性都得到較大提升。(2)K-means聚類方法具有對(duì)起始類中心依靠性大、無(wú)法處理邊界對(duì)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(jìn)行融合后再用于聚類問(wèn)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(jī)分布的方法來(lái)實(shí)現(xiàn),然后調(diào)整學(xué)習(xí)因子、引入隨機(jī)粒子,增加種群的多樣性。最后將改進(jìn)后的算法與粒子群和粗糙集結(jié)合,并用之來(lái)優(yōu)化K-means。實(shí)驗(yàn)結(jié)果表明:該算法在一定程度上弱化了對(duì)原始聚類中心的依賴,能有效地整理邊界數(shù)據(jù),準(zhǔn)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(xué)位授予單位】：長(zhǎng)沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18;TP311.13

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