基于PSO的雙向聚類算法研究
發(fā)布時(shí)間：2018-12-24 13:24
【摘要】：生物信息學(xué)是一門結(jié)合了生物學(xué)、計(jì)算機(jī)科學(xué)、數(shù)學(xué)和化學(xué)等領(lǐng)域知識(shí)的交叉學(xué)科。隨著科技的飛速發(fā)展,基因測(cè)序技術(shù)的研究取得了重大突破,人們逐漸開始對(duì)基因的功能和內(nèi)在機(jī)理展開了探索研究。目前,每天都會(huì)產(chǎn)生海量的基因信息數(shù)據(jù),生命科學(xué)的研究重點(diǎn)也從如何獲取生物數(shù)據(jù)轉(zhuǎn)移到了怎樣對(duì)這些數(shù)據(jù)進(jìn)行有效的分析上面。目前,對(duì)基因表達(dá)數(shù)據(jù)的分析處理,主要采用的方法是聚類。一般的聚類只能從基因矩陣的行或者列單一方向進(jìn)行,這種方法只能找到基因表達(dá)數(shù)據(jù)矩陣中的全局信息。而大量有價(jià)值的生物學(xué)信息往往就隱藏在這些局部信息中,雙向聚類是一種能有效解決該類問題的新興手段。隨著雙向聚類得到越來越多的應(yīng)用,現(xiàn)存算法的缺點(diǎn)與不足也逐漸暴露了出來,因此研究雙向聚類問題是很有必要的。本文的研究目的是利用粒子群算法解決雙向聚類問題,并通過一系列實(shí)驗(yàn)對(duì)比說明了結(jié)合粒子群優(yōu)化的雙向聚類算法的優(yōu)越性。本文主要做的工作如下:(1)雙向聚類算法是一種局部搜索算法,對(duì)于繁雜的基因數(shù)據(jù)矩陣,直接對(duì)其整體進(jìn)行雙向聚類,計(jì)算量大且聚類效果不理想。本文在粒子群算法的基礎(chǔ)上,使用總體類間差異先對(duì)整個(gè)基因矩陣全局尋優(yōu),找出有一定相似性的基因子矩陣,再對(duì)其進(jìn)行添加或刪除行列的操作。使得到的雙向聚類結(jié)構(gòu)更加規(guī)整,避免了基因表達(dá)數(shù)據(jù)不均衡分類的情況。(2)雙向聚類算法是一種多目標(biāo)優(yōu)化算法,FLOC算法作為經(jīng)典雙向聚類算法之一,卻不能很好的同時(shí)優(yōu)化多個(gè)目標(biāo)。結(jié)合PSO算法,對(duì)FLOC算法的目標(biāo)函數(shù)做出修改,提出了PSO-FLOC聚類算法,通過實(shí)驗(yàn)對(duì)比發(fā)現(xiàn),PSO-FLOC算法對(duì)多目標(biāo)優(yōu)化問題表現(xiàn)更佳,并對(duì)算法中參數(shù)的取值進(jìn)行了討論。(3)在粒子群算法中,粒子只能沿著特定的軌跡搜索,從而不能保證以概率1收斂到全局最優(yōu),甚至不能收斂到局部最優(yōu)。為了提高算法的全局搜索能力,結(jié)合具有量子行為的粒子群優(yōu)化算法,形成了QPSO-FLOC聚類算法,并通過實(shí)驗(yàn)與PSO算法進(jìn)行了分析比較,證明QPSO-FLOC算法能取得更好的聚類效果。
[Abstract]:Bioinformatics is an interdisciplinary subject that combines knowledge in biology, computer science, mathematics and chemistry. With the rapid development of science and technology, great breakthrough has been made in gene sequencing technology. At present, huge amounts of genetic data are produced every day, and the research focus of life science has shifted from how to obtain biological data to how to effectively analyze these data. At present, clustering is the main method to analyze and process gene expression data. The general clustering can only be carried out in the single direction of the row or column of the gene matrix. This method can only find the global information in the gene expression data matrix. However, a large amount of valuable biological information is often hidden in these local information. Bidirectional clustering is a new method to solve this kind of problem effectively. With more and more applications of bidirectional clustering, the shortcomings and shortcomings of the existing algorithms are gradually exposed, so it is necessary to study the bidirectional clustering problem. The purpose of this paper is to solve the bidirectional clustering problem with particle swarm optimization (PSO), and the superiority of bidirectional clustering algorithm combined with PSO is illustrated by a series of experiments. The main work of this paper is as follows: (1) Bidirectional clustering algorithm is a kind of local search algorithm. On the basis of particle swarm optimization (PSO), the global optimization of the whole gene matrix is carried out by using the difference of the whole class, and the gene submatrix with certain similarity is found, and then the operation of adding or deleting the column and column is carried out. The bidirectional clustering structure is more regular, and the unbalanced classification of gene expression data is avoided. (2) Bidirectional clustering algorithm is a multi-objective optimization algorithm. FLOC algorithm is one of the classical bidirectional clustering algorithms. But not very good at the same time optimization of multiple goals. Combined with the PSO algorithm, the objective function of the FLOC algorithm is modified and the PSO-FLOC clustering algorithm is proposed. Through the comparison of experiments, it is found that the PSO-FLOC algorithm performs better for the multi-objective optimization problem. The parameters of the algorithm are discussed. (3) in PSO, the particle can only be searched along a specific trajectory, thus the probability 1 can not converge to the global optimal, or even to the local optimal. In order to improve the global searching ability of the algorithm, the QPSO-FLOC clustering algorithm is formed by combining the particle swarm optimization algorithm with quantum behavior. The experimental results show that the QPSO-FLOC algorithm can achieve better clustering effect compared with the PSO algorithm.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13
[Abstract]:Bioinformatics is an interdisciplinary subject that combines knowledge in biology, computer science, mathematics and chemistry. With the rapid development of science and technology, great breakthrough has been made in gene sequencing technology. At present, huge amounts of genetic data are produced every day, and the research focus of life science has shifted from how to obtain biological data to how to effectively analyze these data. At present, clustering is the main method to analyze and process gene expression data. The general clustering can only be carried out in the single direction of the row or column of the gene matrix. This method can only find the global information in the gene expression data matrix. However, a large amount of valuable biological information is often hidden in these local information. Bidirectional clustering is a new method to solve this kind of problem effectively. With more and more applications of bidirectional clustering, the shortcomings and shortcomings of the existing algorithms are gradually exposed, so it is necessary to study the bidirectional clustering problem. The purpose of this paper is to solve the bidirectional clustering problem with particle swarm optimization (PSO), and the superiority of bidirectional clustering algorithm combined with PSO is illustrated by a series of experiments. The main work of this paper is as follows: (1) Bidirectional clustering algorithm is a kind of local search algorithm. On the basis of particle swarm optimization (PSO), the global optimization of the whole gene matrix is carried out by using the difference of the whole class, and the gene submatrix with certain similarity is found, and then the operation of adding or deleting the column and column is carried out. The bidirectional clustering structure is more regular, and the unbalanced classification of gene expression data is avoided. (2) Bidirectional clustering algorithm is a multi-objective optimization algorithm. FLOC algorithm is one of the classical bidirectional clustering algorithms. But not very good at the same time optimization of multiple goals. Combined with the PSO algorithm, the objective function of the FLOC algorithm is modified and the PSO-FLOC clustering algorithm is proposed. Through the comparison of experiments, it is found that the PSO-FLOC algorithm performs better for the multi-objective optimization problem. The parameters of the algorithm are discussed. (3) in PSO, the particle can only be searched along a specific trajectory, thus the probability 1 can not converge to the global optimal, or even to the local optimal. In order to improve the global searching ability of the algorithm, the QPSO-FLOC clustering algorithm is formed by combining the particle swarm optimization algorithm with quantum behavior. The experimental results show that the QPSO-FLOC algorithm can achieve better clustering effect compared with the PSO algorithm.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13
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相關(guān)期刊論文 前10條
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4 周，
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