本文選題：搜索結(jié)果 + 聚類��； 參考：《北京郵電大學》2014年碩士論文

【摘要】：隨著網(wǎng)絡(luò)的發(fā)展,越來越多的人們在互聯(lián)網(wǎng)上獲取信息。搜索引擎作為用戶與互聯(lián)網(wǎng)交互的中轉(zhuǎn)站,負責信息的獲取和檢索,給人們帶來了極大的便利。但是,隨著互聯(lián)網(wǎng)上信息量的增長,搜索引擎返回的檢索結(jié)果也日益繁雜,包含了很多不相干的、·重復的、混雜的結(jié)果。人們往往需要浪費很多的精力和時間來瀏覽這些信息才能找到滿意的結(jié)果。因此,一些研究人員將信息檢索中的聚類技術(shù)應(yīng)用于搜索結(jié)果的分類中,將繁雜的搜索結(jié)果分類呈現(xiàn)給用戶,這種方法稱為搜索結(jié)果聚類。搜索結(jié)果聚類是指利用聚類這種無監(jiān)督的機器學習手段,按照“最大化類內(nèi)相似度,最小化類間相似度”的原則,將搜索結(jié)果聚集成類提取聚類標簽給予用戶一個類目導航。另外,搜索結(jié)果聚類對象不是傳統(tǒng)的長文本而是搜索結(jié)果的短文摘。目前,搜索結(jié)果聚類技術(shù)多是采用獨立的詞語表示搜索結(jié)果短文摘,忽略了詞語之間的語義關(guān)聯(lián)等語義信息,存在嚴重的語義缺失。 本論文針對搜索結(jié)果聚類技術(shù)中的語義缺失現(xiàn)象,對基于語義的搜索結(jié)果聚類方法進行了深入研究,主要的研究內(nèi)容有：搜索結(jié)果預處理方法和建模方法,經(jīng)典的搜索結(jié)果聚類方法以及基于語義的搜索結(jié)果聚類方法。另外,本論文在以上研究的基礎(chǔ)上提出了基于OPTICS的搜索結(jié)果聚類算法和基于WordNet的后綴樹聚類算法。這兩種算法針對搜索結(jié)果聚類的語義缺失現(xiàn)象均提出了相應(yīng)的改進,側(cè)重于挖掘和利用搜索結(jié)果短文摘中的語義信息,以達到提高搜索結(jié)果聚類準確率的目的。最后,本論文在搜索結(jié)果數(shù)據(jù)集上進行了聚類實驗,并對比分析了兩種新算法的聚類性能。實驗結(jié)果表明,本論文中提出的兩種改進算法在聚類準確率方面較原算法有明顯提高,并且縮短了運行時間,能夠提高搜索結(jié)果聚類的可瀏覽性和實時性。
[Abstract]:With the development of the network, more and more people get information on the Internet. As the transfer station of the interaction between the user and the Internet, the search engine is responsible for the acquisition and retrieval of information, which has brought great convenience to people. However, with the increase of the amount of information on the Internet, the retrieval results of the search engine return are also increasingly complex, including a lot of information. Unrelated, repetitive, mixed results. People often need to waste a lot of energy and time to browse the information in order to find satisfactory results. Therefore, some researchers apply clustering techniques in information retrieval to the classification of search results, and classify the complex search results to users. This method is called search. The clustering of search results is an unsupervised machine learning method based on clustering. According to the principle of "maximizing the intra class similarity, minimizing the similarity between classes", the search results are aggregated into classes to extract clustering tags to give users a category navigation. In addition, the search result clustering object is not the traditional long text but the traditional long text. At present, most of the search results clustering techniques use independent words to express search results, ignore semantic information and semantic information between words, and have serious semantic loss.
In this paper, the semantic based search results clustering method is studied deeply in the search result clustering technology. The main research contents are: search results preprocessing method and modeling method, classic search result clustering method and semantic based search result clustering method. On the basis of the research, the OPTICS based search results clustering algorithm and the WordNet based suffix tree clustering algorithm are proposed. These two algorithms have proposed corresponding improvements to the semantic missing phenomenon of the search results clustering, focusing on mining and utilizing the semantic information in the search results short text, in order to improve the clustering accuracy of the search results. Finally, this paper carries out clustering experiments on the data set of the search results, and compares and analyzes the clustering performance of the two new algorithms. The experimental results show that the two improved algorithms proposed in this paper are significantly higher in clustering accuracy than those of the original algorithm, and the running time is shortened, and the clustering of the search results can be improved. Browsing and real-time.

【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.1

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