frequent pattern 中文意思是什麼

frequent pattern 解釋
頻繁模式
  • frequent : adj. 1. 屢次的，常見的；頻繁的。2. （脈搏等）急促的，快的。vt. 1. 常去，時常出入于。2. 與…時常交際[來往]。adv. -ly
  • pattern : n 1 模範，榜樣；典範。2 型，模型；模式；雛型；【冶金】原型。3 花樣；式樣；（服裝裁剪的）紙樣；圖...
  1. In sequential pattern, we describe mfr and the algorithm on how to find frequent access paths. in mining association rules, we introduce famous apriori algorithm and propose the optimized dhp algorithm with hashing

    在關聯規則的挖掘中，深入的分析了經典的apriori演算法，並運用哈希技術改進它得到dhp演算法，其中詳細闡述了該演算法改進的思路。
  2. These researches will help us to discover changing or developing principle of things, support to decision - making, etc. the thesis addresses several key technical problems of pattern mining and its search based similarity in time series, which covers feature patterns and relationship patterns mining, pattern search based similarity in time series and stream time series and issues concerning application system implementation oriented to analysis. major contributions of this thesis include : 1. research of mining feature patterns in time series a novel method is proposed to discovery frequent pattern from time series

    本文在分析時間序列特點和實際應用需求的基礎上，針對時間序列的挖掘與相似性查找一些關鍵技術進行了研究，具體包括特徵模式挖掘、多序列關聯模式挖掘、相似性模式查找等方面，所做的工作和取得的創新成果體現在以下三個方面： 1 ）時間序列特徵模式挖掘研究首次提出了一種基於互關聯后繼樹模型的時序特徵模式挖掘方法。
  3. These researches will help us to discover changing or developing principle of things, support to decision - making, etc. the thesis addresses several key technical problems of pattern mining and its search based similarity in time series, which covers feature patterns and relationship patterns mining, pattern search based on similarity in time series and stream time series and issues concerning application system implementation oriented to analysis. major contributions of this thesis include : 1. research of mining feature patterns in time series a novel method is proposed to discovery frequent pattern from time series

    本文在分析時間序列特點和實際應用需求的基礎上，針對時間序列的挖掘與相似性查找一些關鍵技術進行了研究，具體包括特徵模式挖掘、多序列關聯模式挖掘、相似性模式查找，在線相似性查找以及最終的分析應用系統開發等方面，所做的工作和取得的創新成果體現在以下五個方面： 1 ）時間序列特徵模式挖掘研究首次提出了一種基於互關聯后繼樹模型的時序特徵模式挖掘方法。
  4. In addition, comparing to direct using fp _ growth algorithms, this algorithm has no need to expand negative item to original database, and construc or destruct additional data structures, which only make some changes on the original frequent pattern tree, so it has certain advantages in time and space costs

    除此之外，該演算法與直接使用fp _ growth演算法挖掘含負項目的頻繁項集演算法相比，無需對原始數據庫進行負項目的擴展，也不用再構造並銷毀額外的數據結構，只需在原始的頻繁模式樹上修改，在時間和空間的開銷上都具有一定的優勢。
  5. A recurrent, often unconscious pattern of behavior that is acquired through frequent repetition

    習慣一種由於不斷的重復而形成的反復出現，經常是無意識的行為方式
  6. In tcm this pattern is called pair of medicine, and it can be resolved by frequent pattern mining. the symptom complex diagnose can be treated as a bayesian training and a bayesian classification on large clinical database cases. the critical step to resolve the chinese prescription compounding is to build an appropriate model to express the progress of it

    中藥知識發現集中在發現常用的單味藥合用模式，在中醫術語中稱之為藥對，這可以用高頻集發現來解決；中醫癥候診斷可以看成是在大量臨床案例庫上的貝葉斯訓練器和分類器；解決方劑配伍問題的關鍵是建立起一個合適的配伍計算機模型。
  7. It is based on the fact that unique labeled graph can be transformed into the format of itemset, on which recent 10 years research on frequent pattern mining can be applied

    由於唯一標號圖能轉換為項集的形式，這就能充分利用近10年來的研究成果。唯一不同的地方是在連通性上的進一步考慮。
  8. Based on the analysis of above drawbacks, this paper proposes frequent access pattern tree algorithm ( fapt ). this algorithm includes two steps : access pattern tree method, through the pattern matching method it saves user ' s visit sequences with tree ; pruning method, it uses frequent degree to prune access pattern tree which is under the frequent degree

    在分析以上不足的基礎上，提出了頻繁訪問模式樹（ fapt ）演算法，該演算法包括以下兩個步驟:訪問模式樹的生成，通過模式匹配的方法將用戶訪問序列以樹形結構來存儲;修剪的策略，利用頻繁度對訪問模式樹進行修剪，修剪掉其中低於頻繁度的節點。
  9. All urge people to put more and more attentions to the frequent pattern mining in graphs

    同時，隨著各種新應用的不斷推出，人們將注意力逐步向圖中的頻繁模式的產生問題轉移。
  10. Frequent pattern mining technology in data mining is for mining characteristic patterns with frequent occurrences among data

    數據挖掘中的頻繁模式挖掘技術專注于發現數據中頻繁出現的特徵模式。
  11. When there are a great many of items and transactions in the database, frequent - pattern growth algorithm needs more additional computer memory, which may cause the lack of memory

    當數據庫中的項目數目較大且事務數量巨大時，頻繁模式增長演算法內存開銷很大，可能導致內存空間不足的現象。
  12. Next, after near 10 years research and development, the most essential phase in association rules mining, frequent pattern acquirement, and its techniques have been improved dramatically

    其次，在經歷了近10年的發展以後，關聯規則挖掘中至關重要的頻繁模式獲取技術得到了很大的發展。
  13. The work in the dissertation is strictly bounded in such field by following the two phases, frequent pattern acquirement and rules generation, to deep into the extended research step by step

    本文的工作在關聯規則挖掘的范疇以內，根據關聯規則的生成的二個主要階段：頻繁模式的獲取和關聯規則的生成進行了深入的拓展性研究。
  14. The algorithm is based on the frequent pattern tree, which uses for reference a compressed storage data structure i. e. frequent pattern tree of fp _ growth algorithm. it mines frequent item sets with negative items through extending frequent patterns on the tree

    該演算法借用fp _ growth演算法中頻繁模式樹這種壓縮存儲事務的數據結構，通過頻繁模式樹進行模式擴展，挖掘含負項目的頻繁項集。
  15. This paper discussed the clustering algorithms based on the longest frequent closed itemsets using frequent - pattern tree, concluded that in some situation it is inapproriate to using this technique to classifing data in data mining

    摘要針對採用頻繁模式樹構造的最長頻繁閉項集的聚類演算法，提出該演算法在一些特殊環境下可能產生的誤差，因而建議在一些應用情況下，不宜採用該演算法進行數據挖掘中的數據分類。
  16. The algorithm eliminates the redundancy brought by isomorphic overlapped sub trees, and assures the minimum of frequent pattern

    本演算法剔除了同構交疊子樹帶來的冗餘，保持了模式在一棵樹上的最小性。
  17. The paper summarizes the history of frequent pattern mining technology and the development of frequent sub tree mining algorithms ; introduces the main methods of rna secondary structure prediction ; analyzes the deficiency of the applications of data mining in bioinformatics

    本文首先概括了頻繁模式挖掘技術及頻繁子樹挖掘演算法的現狀，介紹了rna二級結構預測的主要方法，探討了目前數據挖掘技術應用於生物數據所存在的問題。
  18. Data mining is a collection of engineering methods proposed for solving large practical problems of machine learning, pattern recognition, database technology etc. the purpose of data mining is to discover the implicit knowledge or rule, and to help human expert to make decision. frequent pattern mining ( fpm ) and bayesian network learning ( bnl ) are two useful technologies of data mining

    數據挖掘技術是解決機器學習、模式識別、數據庫技術等各種領域中的大型實際應用問題而提出的一些工程性方法的集合，主要是為了從大型數據庫中高效地發現隱含在其中的知識或規律，並為人類專家的決策提供支持。
  19. Efficient mining algorithm for frequent pattern in intrusion detection

    基於最小完美哈希函數的數據挖掘演算法
  20. A few classic association rule extracting algorithms are deeply discussed. in order to resolve frequent pattern mining problem efficiently, redundant operation and temporary data in fp - growth algorithm are analyzed, data structures, fpr - tree and fpr - list, are imported, the method of conditional pattern base generation and storage in fp - growth algorithm is improved, and a new fprsg algorithm is presented

    為解決頻繁模式挖掘問題，本文通過分析fp - growth演算法中包含的冗餘操作，引入數據結構fp參考樹/表，改變fp - growth演算法中條件模式基的存儲和生成方式，提出了新的fprsg演算法，高效地解決了頻繁模式挖掘問題。
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