本文選題：細(xì)菌覓食優(yōu)化算法　切入點(diǎn)：聚類(lèi)　出處：《廣西民族大學(xué)》2017年碩士論文

【摘要】：隨著網(wǎng)絡(luò)交易的盛行和B2C的出現(xiàn),網(wǎng)絡(luò)上出現(xiàn)的紛繁復(fù)雜的信息有時(shí)會(huì)讓人難以理解和運(yùn)用,而具有不同要求和目的的用戶(hù)的行為模式也不盡相同。分析網(wǎng)絡(luò)用戶(hù)行為模式最有效的途徑之一就是聚類(lèi)分析。通過(guò)輿情分析中的聚類(lèi)可以探尋用戶(hù)的行為習(xí)慣、需求和喜好,更好的幫助網(wǎng)站開(kāi)發(fā)者有針對(duì)性的規(guī)劃網(wǎng)站,進(jìn)而改善用戶(hù)的上網(wǎng)瀏覽體驗(yàn)。比如在一些大型的購(gòu)物網(wǎng)站,存在著許多不同種類(lèi)的用戶(hù)行為,包括無(wú)目標(biāo)性的隨機(jī)瀏覽商品的用戶(hù);具有特定網(wǎng)購(gòu)目標(biāo)的瀏覽用戶(hù);待購(gòu)商品在購(gòu)物車(chē)中的用戶(hù)等。上面的舉例是為了說(shuō)明網(wǎng)站可通過(guò)分析用戶(hù)的行為模式了解不同用戶(hù)的需求和心理,更有針對(duì)性的改善網(wǎng)站的布局和內(nèi)容,從而促進(jìn)產(chǎn)品的推廣和銷(xiāo)售。K-means是一種應(yīng)用頗為廣泛的數(shù)據(jù)聚類(lèi)算法。K-means算法以及改進(jìn)的K-means算法,在對(duì)海量數(shù)據(jù)集聚類(lèi)時(shí)總會(huì)面臨容易陷入局部最優(yōu)的問(wèn)題。群智能優(yōu)化技術(shù)應(yīng)運(yùn)而生,它是借鑒了不同動(dòng)物或昆蟲(chóng)的各種生物本能行為,從而建立一個(gè)數(shù)學(xué)模型來(lái)解決實(shí)際問(wèn)題,克服了傳統(tǒng)經(jīng)典算法無(wú)法搜索到全局最優(yōu)解的缺陷。本文提出改進(jìn)的細(xì)菌覓食算法,將對(duì)原有的細(xì)菌覓食算法進(jìn)行改進(jìn),改進(jìn)原有算法的不足,有效提高了聚類(lèi)的收斂速度和準(zhǔn)確度,本文主要的工作及特色如下:(1)本文提出了一種改進(jìn)的細(xì)菌覓食聚類(lèi)算法,對(duì)原算法中的趨向性操作、復(fù)制操作和遷徙操作進(jìn)行改進(jìn),改善聚類(lèi)精度和收斂速度。實(shí)驗(yàn)中將引入多種不同數(shù)據(jù)集對(duì)改進(jìn)后的算法有效性進(jìn)行測(cè)試,細(xì)菌覓食算法的參數(shù)選取問(wèn)題也將得到改進(jìn)。聚類(lèi)后將實(shí)驗(yàn)結(jié)果與其他常用的算法對(duì)比,驗(yàn)證本文改進(jìn)算法的有效性。(2)將改進(jìn)后的細(xì)菌覓食算法應(yīng)用于輿情分析,建立熱度評(píng)價(jià)模型并用改進(jìn)的算法對(duì)網(wǎng)頁(yè)頁(yè)面進(jìn)行聚類(lèi),最后設(shè)置實(shí)驗(yàn)從時(shí)間和正確率等方面對(duì)改進(jìn)算法的有效性進(jìn)行分析和驗(yàn)證。
[Abstract]:With the popularity of network transactions and the emergence of B2C, the complicated information on the network sometimes makes people difficult to understand and use, and the behavior patterns of users with different requirements and purposes are also different.One of the most effective ways to analyze the behavior patterns of network users is clustering analysis.Through the clustering in the analysis of public opinion, we can explore the behavior habits, needs and preferences of users, and better help website developers to plan their websites, and then improve the browsing experience of users.For example, in some large shopping websites, there are many different kinds of user behaviors, including random browsing users, users with specific online shopping targets, users in shopping cart and so on.The above example is to show that the website can understand the needs and psychology of different users by analyzing the user's behavior patterns, and improve the layout and content of the website more pertinently.Thus to promote the promotion and sale of products. K-means is a widely used data clustering algorithm. K-means algorithm and improved K-means algorithm.The swarm intelligence optimization technique arises as the times require. It draws lessons from various biological instinctive behaviors of different animals or insects, and thus establishes a mathematical model to solve practical problems, and overcomes the defects of traditional classical algorithms that cannot find the global optimal solution.In this paper, an improved bacterial foraging algorithm is proposed, which will improve the original bacterial foraging algorithm, improve the shortcomings of the original algorithm, and effectively improve the convergence speed and accuracy of clustering.The main work and features of this paper are as follows: (1) in this paper, an improved bacterial foraging clustering algorithm is proposed, which improves the trend operation, replication operation and migration operation in the original algorithm, and improves the clustering accuracy and convergence speed.In the experiment, a variety of different data sets will be introduced to test the effectiveness of the improved algorithm, and the parameter selection of the bacterial foraging algorithm will also be improved.After clustering, the experimental results are compared with other commonly used algorithms to verify the effectiveness of the improved algorithm. The improved bacterial foraging algorithm is applied to the analysis of public opinion, and the heat evaluation model is established and the improved algorithm is used to cluster the web pages.Finally, the effectiveness of the improved algorithm is analyzed and verified in terms of time and accuracy.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

【參考文獻(xiàn)】

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本文編號(hào)：1703461