本文關鍵詞： 數(shù)據(jù)挖掘 關聯(lián)分析 頻繁1-項集 增量式數(shù)據(jù)模式 節(jié)省時間　出處：《吉林大學》2017年碩士論文　論文類型：學位論文

【摘要】：從人類文明誕生起,人類對數(shù)據(jù)的收集以及相應的分析都在進行著。比如古人對天氣的總結以及預測都是在人們?nèi)粘Ｉ钪袑μ鞖庾兓闆r也就是天氣變化的數(shù)據(jù)的收集以及分析得到的結論;還有農(nóng)作物的種植時間、種植方法等也是人們根據(jù)歷年來種植的情況以及經(jīng)驗總結出來的方式方法,這也是對數(shù)據(jù)的收集以及分析的過程;其余的還有建筑、水利、商業(yè)等等,自古以來人們對數(shù)據(jù)的收集以及使用體現(xiàn)在生活的方方面面。在互聯(lián)網(wǎng)出現(xiàn)之前,人們對數(shù)據(jù)的使用大都局限在一個區(qū)域范圍內(nèi),區(qū)域內(nèi)的天氣、地域性的農(nóng)作物以及適應氣候的建筑風格等。伴隨著互聯(lián)網(wǎng)的出現(xiàn)與發(fā)展,伴隨著世界信息一體化的形成,人們可以更方便的得到更多更有用的數(shù)據(jù),這也就意味著更多有價值的東西會從數(shù)據(jù)中得到,也就是現(xiàn)在的數(shù)據(jù)挖掘。數(shù)據(jù)挖掘意在發(fā)現(xiàn)數(shù)據(jù)中的價值,主要有聚類分析、分類分析、關聯(lián)分析、預測以及偏差分析等。其中關聯(lián)分析是對數(shù)據(jù)中相關的項進行總結,從而進行其他的分析工作,也是與本文相關的方向。為了方便關聯(lián)規(guī)則分析,出現(xiàn)了很多關聯(lián)分析的算法,意在找到數(shù)據(jù)中關聯(lián)性強的數(shù)據(jù)項。在大多數(shù)關聯(lián)規(guī)則算法中都需要先生成頻繁1-項集,隨后在生成的頻繁1-項集的基礎上繼續(xù)進行后續(xù)的工作。對于只用進行一次的關聯(lián)規(guī)則分析,頻繁1-項集的生成需要對數(shù)據(jù)庫進行一次掃描,但是在數(shù)據(jù)不斷增加、關聯(lián)分析持續(xù)進行的情況下,每次關聯(lián)分析時都要在生成頻繁1-項集時掃描數(shù)據(jù)庫,這也就意味著后續(xù)的關聯(lián)分析中需要對舊的數(shù)據(jù)進行重復的掃描工作,這必將浪費很多時間。本文就針對這一情況對頻繁1-項集的生成進行了改進,以達到節(jié)省不必要的數(shù)據(jù)庫讀取掃描時間。對于增量式數(shù)據(jù)情況下的頻繁1-項集生成的改進主要是通過在生成頻繁1-項集過程中的候選1-項集的數(shù)據(jù)轉存來實現(xiàn),運用的原理是數(shù)據(jù)條目數(shù)要遠遠大于數(shù)據(jù)項種類數(shù),從而節(jié)省了后續(xù)的關聯(lián)規(guī)則分析時生成頻繁1-項集的時間,從而節(jié)省整個算法的工作時間。
[Abstract]:Since the birth of human civilization. Human data collection and corresponding analysis are going on. For example, the ancient summary and prediction of the weather is the collection and analysis of weather change in people's daily life, that is, weather change data. Conclusions reached; There is also crop planting time, planting methods and so on are also based on the past years of cultivation and experience summed up the way and methods, which is also the process of data collection and analysis; The rest are buildings, water conservancy, commerce and so on, and the collection and use of data since ancient times has been reflected in all aspects of life. Before the advent of the Internet. People's use of data is limited to a regional scope, the regional weather, regional crops and climate adaptation of the architectural style. With the emergence and development of the Internet. With the formation of information integration in the world, people can easily get more and more useful data, which means that more valuable things will be obtained from the data. That is, the current data mining. Data mining is intended to find the value of data, mainly cluster analysis, classification analysis, association analysis. Prediction and deviation analysis. Among them, association analysis is to summarize the related items in the data, so as to carry out other analysis work, which is also related to the direction of this paper. In order to facilitate the analysis of association rules. There are many association analysis algorithms to find data items with strong correlation in data. In most association rules algorithms we need to generate frequent 1-item sets first. Then we continue to do the following work on the basis of the generated frequent 1-itemsets. For only one association rule analysis, the frequent 1-itemsets generation needs to scan the database once. However, when the data is increasing and the association analysis is ongoing, the database must be scanned at the time of generating frequent 1-item sets each time the association analysis is carried out. This means that the follow-up association analysis needs to scan the old data repeatedly, which will waste a lot of time. In this paper, we improve the generation of frequent 1-item sets. In order to save unnecessary scanning time of database reading. The improvement of frequent 1itemsets generation in the case of incremental data is mainly through the data transfer of candidate 1-itemsets in the process of generating frequent 1-itemsets. To make it happen. The principle is that the number of data items is much larger than the number of data items, thus saving the time of generating frequent 1-item sets in subsequent association rule analysis, thus saving the working time of the whole algorithm.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13

【參考文獻】

相關期刊論文 前10條

1 宋余慶,朱玉全,孫志揮,陳耿;基于FP-Tree的最大頻繁項目集挖掘及更新算法[J];軟件學報;2003年09期

2 呂佳;;Web日志挖掘技術應用研究[J];重慶師范大學學報(自然科學版);2006年04期

3 安建成;劉超慧;;頻繁項集快速挖掘及更新算法[J];微電子學與計算機;2008年06期

4 余平;汪繼文;;Apriori算法的一種改進研究[J];廊坊師范學院學報(自然科學版);2009年04期

5 錢秀檳;李錦川;方星;;信息安全事件定位中的Web日志分析方法[J];信息網(wǎng)絡安全;2010年06期

6 呂艷華;衛(wèi)榮娟;;基于知識獲取障礙分析的學術服務對策研究[J];中華醫(yī)學圖書情報雜志;2011年10期

7 賈l，

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