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GHIC: A Hierarchical Pattern Based Clustering Algorithm for Grouping Web Transactions

机译：GHIC：基于分层模式的Web交易分组聚类算法

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Yinghui Yang; Balaji Padmanabhan;
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年度 2008
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1. GHIC: a hierarchical pattern-based clustering algorithm for grouping Web transactions [J] . Yang Y., Balaji Padmanabhan IEEE Transactions on Knowledge and Data Engineering . 2005,第9期

机译：GHIC：一种基于分层模式的集群算法，用于对Web交易进行分组
2. Performance Evaluation of Hierarchical SOAP Based Web Service in Load Balancing Cluster-Based and Non-Cluster-Based Web Server [J] . Tulshi Bezboruah, Abhijit Bora International journal of information retrieval research . 2015,第4期

机译：基于负载平衡基于群集和基于非群集的Web服务器中基于分层SOAP的Web服务的性能评估
3. Parallelization of a graph-cut based algorithm for hierarchical clustering of web documents [J] . Karthick Seshadri, S. Mercy Shalinie Concurrency and computation: practice and experience . 2015,第17期

机译：Web文档分层聚类的基于图割的算法的并行化
4. Web Page Clustering: A Hyperlink-Based Similarity and Matrix-Based Hierarchical Algorithms [C] . Jingyu Hou, Yanchun Zhang, Jinli Cao 5th Asia-Pacific Web Conference on Web Technologies and Applications APWeb 2003 Apr 23-25, 2003 Xian, China . 2003

机译：网页聚类：基于超链接的相似性和基于矩阵的分层算法
5. Design of a Scalable, Configurable, and Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm and Recurrent Neural Networks [D] . Dey, Sumon. 2019

机译：设计可扩展，可配置和基于群集的分层硬件加速器，用于显影灵感算法和经常性神经网络
6. Using Hierarchical Cluster Models to Systematically Identify Groups of Jobs With Similar Occupational Questionnaire Response Patterns to Assist Rule-Based Expert Exposure Assessment in Population-Based Studies [O] . Melissa C. Friesen, Susan M. Shortreed, David C. Wheeler, -1

机译：使用层次聚类模型系统地识别具有类似职业调查问卷响应模式的工作组以协助基于人口的研究中基于规则的专家暴露评估
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。

GHIC: A Hierarchical Pattern Based Clustering Algorithm for Grouping Web Transactions

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