本文選題：細菌覓食優(yōu)化算法　切入點：聚類　出處：《廣西民族大學》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)雜的信息有時會讓人難以理解和運用,而具有不同要求和目的的用戶的行為模式也不盡相同。分析網(wǎng)絡(luò)用戶行為模式最有效的途徑之一就是聚類分析。通過輿情分析中的聚類可以探尋用戶的行為習慣、需求和喜好,更好的幫助網(wǎng)站開發(fā)者有針對性的規(guī)劃網(wǎng)站,進而改善用戶的上網(wǎng)瀏覽體驗。比如在一些大型的購物網(wǎng)站,存在著許多不同種類的用戶行為,包括無目標性的隨機瀏覽商品的用戶;具有特定網(wǎng)購目標的瀏覽用戶;待購商品在購物車中的用戶等。上面的舉例是為了說明網(wǎng)站可通過分析用戶的行為模式了解不同用戶的需求和心理,更有針對性的改善網(wǎng)站的布局和內(nèi)容,從而促進產(chǎn)品的推廣和銷售。K-means是一種應(yīng)用頗為廣泛的數(shù)據(jù)聚類算法。K-means算法以及改進的K-means算法,在對海量數(shù)據(jù)集聚類時總會面臨容易陷入局部最優(yōu)的問題。群智能優(yōu)化技術(shù)應(yīng)運而生,它是借鑒了不同動物或昆蟲的各種生物本能行為,從而建立一個數(shù)學模型來解決實際問題,克服了傳統(tǒng)經(jīng)典算法無法搜索到全局最優(yōu)解的缺陷。本文提出改進的細菌覓食算法,將對原有的細菌覓食算法進行改進,改進原有算法的不足,有效提高了聚類的收斂速度和準確度,本文主要的工作及特色如下:(1)本文提出了一種改進的細菌覓食聚類算法,對原算法中的趨向性操作、復(fù)制操作和遷徙操作進行改進,改善聚類精度和收斂速度。實驗中將引入多種不同數(shù)據(jù)集對改進后的算法有效性進行測試,細菌覓食算法的參數(shù)選取問題也將得到改進。聚類后將實驗結(jié)果與其他常用的算法對比,驗證本文改進算法的有效性。(2)將改進后的細菌覓食算法應(yīng)用于輿情分析,建立熱度評價模型并用改進的算法對網(wǎng)頁頁面進行聚類,最后設(shè)置實驗從時間和正確率等方面對改進算法的有效性進行分析和驗證。
[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.
【學位授予單位】：廣西民族大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

【參考文獻】

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本文編號：1703461