Description: Tiger Tree Hash is constructed from two parts, the Tiger Hash Algorithm and Merkle Tree. The original Tiger Hash code was taken from Tiger.Net which is written in Visual Basic. NET.
Tiger Tree Hash算法的C#实现！
-Tiger Tree Hash is constructed from two par ts. the Tiger Hash Algorithm and Merkle Tree. The or iginal Tiger Hash code was taken from Tiger.Net which is written in Visual Basic.NET. Tiger Tre e Hash Algorithm Implementation of C#! Platform: |
Size: 48128 |
Author:成东 |
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Description: WAP树类似于FP-tree,是用于邻近序列模式的挖掘,可以作为相关算法改进的基础-WAP tree similar to FP-tree, is used for excavation adjacent sequential patterns can be used as a basis for improved correlation algorithm Platform: |
Size: 5120 |
Author:王文 |
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Description: 基于候选产生 测试的序列模式挖掘算法，gsp使用序列模式的向下封闭性，采用多次扫描的候选产生测试方法。-Have a test based on a candidate sequential pattern mining algorithm, gsp down the use of closed sequential patterns, and the use of multiple scan generated candidate testing methods. Platform: |
Size: 15360 |
Author:tian |
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Description: ：用户在访问Web站点时会碰到很多问题，主要原因是Web站点对用户需求缺乏适应性。为了提高Web用户的服务质量和用户的满意度，在用户访问网站点击流形成频繁序列模式的基础上，提出基于距离函数的聚类分析以及基于时间相似度函数的二次聚类分析算法。该算法可以求取频繁序列的相关性和反映用户对网页的兴趣的相似度，对下一步改善Web站点的结构及存在形式使站点达到更好的效果起先导作用-: Visit the Web site users will encounter a lot of problems, mainly due to Web sites the lack of adaptability to user needs. In order to improve the Web user s service quality and customer satisfaction, visit the Web site when users click-stream frequent sequential patterns formed on the basis of the distance function based on cluster analysis and time-based similarity function of the second cluster analysis algorithm. This algorithm can find frequent sequences relevance and reflect the user s homepage on the Internet for similarity, the next step to improve the Web site s structure and form so that site to achieve better results from the leading role Platform: |
Size: 152576 |
Author:li |
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Description: SPAM是现在最快找到全频繁序列模式的方法之一。这里是SPAM的源码。-SPAM is now the fastest growing full-frequent sequential patterns to find one of the ways. This is the SPAM source. Platform: |
Size: 1148928 |
Author:howard |
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Description: Speedy, Mini and Totally Fuzzy:
Three Ways for Fuzzy Sequential Patterns Mining in java programming Platform: |
Size: 258048 |
Author:pravin jadhav |
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Description: Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr-Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr Platform: |
Size: 3283968 |
Author:driftinwind |
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Description: In computer science, Communicating Sequential Processes (CSP) is a formal language for describing patterns of interaction in concurrent systems.It is a member of the family of mathematical theories of concurrency known as process algebras, or process calculi. CSP was highly influential in the design of the occam programming language,and also influenced the design of programming languages such as Limbo and Go.Wikipedia Platform: |
Size: 562176 |
Author:glaucia campos |
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Description: A Fast Incremental Mining Algorithm of Sequential Patterns Based on Sequence Tr-A Fast Incremental Mining Algorithm of Sequential Patterns Based on Sequence Tree Platform: |
Size: 131072 |
Author:al |
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Description: AprioriAll算法的基本思路
1) 排序阶段 利用客户标识customer 2id作为主关键字以及事务发生的时间transaction 2 time作为次关键字对数据库D排序,该步骤将原始的事务数据库转换成客户序列的数据库.
2) 发现频繁项集阶段 利用关联规则挖掘算法找出所有的频繁项目集.
3) 转换阶段 在已经转换的客户序列中,每一个事务被包含于该事物中的所大项目集来替换,如果一个序列不包含任何大项目集,则在已经转换的序列中不应该保留这项事务.
4) 序列阶段 利用核心算法找出所有的序列模式.
-Sequential pattern mining from the sequence found in the database as a sequence of frequent pattern, it is a kind of important data mining issues, has a very wide application, be used in customer buying behavior, including the analysis of network access mode of analysis, the scientific experiments Analysis, the early diagnosis of disease, natural disasters forecast, DNA sequences deciphered, and so on. The efficiency. In this paper, I was in the sequence pattern mining one of two algorithms to study, namely: Armorial and GSP algorithm. First on the sequence patterns of some basic concepts and principles. And demonstrate through concrete examples of the implementation of the algorithm, then reached into the grasp of understanding. Used vc again based on the programming language and Access database to achieve the end result of running the analysis and synthesis. Platform: |
Size: 2048 |
Author:hou ruilian |
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Description: 在2006年9月召开的ICDM会议上，邀请了ACM KDD创新大奖(InnovationAward)和
Top 10 Algorithms in Data Mining
IEEEICDM研究贡献奖(Research Contributions Award)的获奖者们来参与数据挖掘10大算
法的选举，每人提名10种他认为最重要的算法-Classification，Statistical Learning，Top 10 Algorithms in Data Mining，materials on Association Analysis，Link Mining，Clustering，Bagging and Boosting，Sequential Patterns，Integrated Mining，Rough Sets，Graph Mining Platform: |
Size: 1840128 |
Author:yz |
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Description: This work presents the current state-of-the-art in techniques for tracking a number of objects moving in
a coordinated and interacting fashion. Groups are structured objects characterized with particular motion
patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport
players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The
group object tracking is closely linked with extended object tracking but at the same time has particular
features which differentiate it extended objects-This work presents the current state-of-the-art in techniques for tracking a number of objects moving in
a coordinated and interacting fashion. Groups are structured objects characterized with particular motion
patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport
players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The
group object tracking is closely linked with extended object tracking but at the same time has particular
features which differentiate it extended objects Platform: |
Size: 1527808 |
Author:Gomaa Haroun |
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Search - sequential patterns