structure mining造句
例句與造句
- to structure mining rights market based on mining property management
以礦業(yè)權資產(chǎn)管理為核心抓好礦業(yè)權市場建設 - finally, we design and implement a universal tree structure mining prototype as well as system testing analysis
最后,設計和實現(xiàn)了通用樹結構挖掘原型系統(tǒng)以及系統(tǒng)的測試分析。 - generally speaking, web mining includes three research domains : web content mining, web structure mining and web usage mining
一般而言,它的研究領域包括web內(nèi)容挖掘、web結構挖掘和web使用挖掘。 - finally, i do experiments to validate the video structure mining method that this paper brings forward, and the result is satisfying
最后通過探討性的實驗，對本文提出的視頻結構挖掘的方法進行了驗證，獲得了較為滿意的結果。 - this paper has researched definition, prototype and technique of video data mining and the high structure mining of video . the mostly work is as follow . 1
本論文對視頻挖掘的概念、一般系統(tǒng)結構及視頻的高層結構挖掘的方法進行了研究，主要工作如下：1 - It's difficult to find structure mining in a sentence. 用structure mining造句挺難的
- the basic element of search engines is the web page, so this paper focuses on time information mining, structure mining and fingerprint mining of web pages
而搜索引擎搜索的最基本元素就是網(wǎng)頁,所以本文從網(wǎng)頁入手,對網(wǎng)頁進行了時間信息挖掘,結構信息挖掘和指紋信息挖掘。 - however, much information is contained in the link-structure of the web pages, from which people can find much useable information through web structure mining technology
然而，web網(wǎng)頁中存在著豐富的超鏈接結構信息，利用web結構挖掘技術可以從中挖掘出有用的信息來改進搜索引擎技術。 - according to the differences between mined objects, the conventional classification method classifies the web mining into three categories : web content mining, web structure mining and web usage mining
傳統(tǒng)的分類方法根據(jù)挖掘對象的不同將web挖掘分為三類:web內(nèi)容挖掘、web結構挖掘和web使用記錄挖掘。 - structured mining plays an important role in xml documents mining, web page traffic mining, analysis of molecular evolution, packet routing, biological informatics, biological computing, communication system, image database and city management
結構數(shù)據(jù)挖掘在xml文檔挖掘,網(wǎng)頁流量挖掘,生物進化的分析,路由選擇,生物信息學,生物計算,通訊系統(tǒng),圖像數(shù)據(jù)庫,城鎮(zhèn)規(guī)劃等諸多領域發(fā)揮重要作用。 - to achieve such a hierarchical structure mining, this paper proposed an efficient shot detection algorithm, developed a novel algorithm for measuring video shot similarity and adopt scene change detection ( scd ) to construct video scene based on color, texture and semantic similarity between shots
摘要為了實現(xiàn)視頻層次結構挖掘，提出了一個有效的視頻鏡頭分割算法和一種鏡頭相似性度量方法，然后根據(jù)鏡頭顏色、紋理和語義相似性采用場景邊界探測算法構造視頻場景。 - so, we combine classical data mining methods with structured mining technologies and improve on canonical form and pre-processing, pruning, growing and mining technologies . based on analysis of prototype system, we can conclude that the demonstration of correctness and validity of algorithm
通過原型系統(tǒng)的設計開發(fā),從而將傳統(tǒng)數(shù)據(jù)挖掘的方法和結構挖掘算法結合起來,改進了樹結構的規(guī)范化和預處理技術、樹結構的剪枝和生長技術、樹結構的挖掘技術,有效地實現(xiàn)了系統(tǒng)設計目的。 - we also place emphasis on research of apriori and fp-growth algorithm and compare the performance of two algorithms . secondly, we do research on concepts of structured and nonstructured data, actual state and problems of tree structure mining, and freetreeminer algorithm theory . we also study canonical form and pre-processing technologies of free-tree, concepts and properties of closed and maximal tree, and pruning, growing and mining of tree structure
其次,研究了結構化與非結構化數(shù)據(jù)的基本概念、樹結構挖掘的研究現(xiàn)狀、現(xiàn)有樹結構挖掘技術存在的問題、freetreeminer算法及其基本思想,重點研究了free樹的規(guī)范化和預處理技術、封閉頻繁子樹和最大頻繁子樹的概念和性質、樹結構的剪枝和生長技術、樹結構的挖掘技術。 - this phenomenon has two effects : first, there are too many frequent subtrees for users to manage and use, and second, an algorithm that discovers all frequent subtrees is not able to handle frequent subtrees with large size . the research work of the dissertation is involved in classical data mining and structured mining and the main contents are as follows : firstly, we investigate data mining concepts and principles, data pre-processing technologies and tasks, objects, methods, tools, processes and problems of data mining
本課題在此背景下,主要對傳統(tǒng)數(shù)據(jù)挖掘技術和結構挖掘技術進行了如下幾個方面的研究:首先,研究了數(shù)據(jù)挖掘的概念與原理、數(shù)據(jù)的預處理技術、數(shù)據(jù)挖掘的任務和對象、數(shù)據(jù)挖掘的方法、數(shù)據(jù)挖掘的工具和步驟、數(shù)據(jù)挖掘中存在的問題,重點研究了apriori算法和fp-growth算法的思想、實現(xiàn)過程,對兩種算法的性能進行了比較。