Finding similar patterns in time stamped temporal datasets

机译：在带有时间戳的时间数据集中找到相似的模式

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

The research objective in this paper is to address the scope for research to design dissimilarity measure which uses the standard score and normal probability. Traditionally, the dissimilarity measure used is Euclidean distance to obtain dissimilarity between two known vectors of m-dimensions. The measure addressed in this paper maps the temporal pattern expressed as support vectors to z-space vectors. The dissimilarity measures uses these z-space vectors to find the dissimilarity degree between any two patterns expressed as support vectors. The procedure to retrieve similar patterns is outlined as the algorithm which uses distance and support bounds to eliminate and prune patterns that are not the required candidate patterns.

机译：本文的研究目标是要解决使用标准得分和正态概率设计差异度量的研究范围。传统上，所使用的相异性度量为欧几里得距离，以获取两个已知的m维向量之间的相异性。本文提出的措施将表示为支持向量的时间模式映射到z空间向量。差异度度量使用这些z空间向量来查找表示为支持向量的任何两个模式之间的差异度。检索相似模式的过程概述为使用距离和支持范围来消除和修剪不是必需的候选模式的模式的算法。

著录项

来源
《International Conference on Engineering MIS》|2017年|1-5|共5页
会议地点 Monastir(TN)
作者
Shadi. A. Aljawarneh; V. Radha Krishna; Aravind Cheruvu;
展开▼
作者单位

Software Engineering Dept JUST Jordan;

Department of IT VNR VJIET India;

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词
Itemsets; Data mining; Standards; Support vector machines; Market research; Task analysis;

机译：项目集；数据挖掘;标准；支持向量机；市场调查;任务分析;

相似文献

外文文献
中文文献
专利

1. Efficient constraint-based Sequential Pattern Mining (SPM) algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset [J] . Niti Ashish Kumar Desai, Amit Ganatra Cogent Engineering . 2015,第1期

机译：基于约束的高效序列模式挖掘（SPM）算法，可从基于时间戳的序列数据集中了解客户的购买行为
2. Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints [J] . Sofya S. Titarenko, Valeriy N. Titarenko, Georgios Aivaliotis, Journal of Big Data . 2019,第1期

机译：具有时间戳不确定性和时间约束的模式挖掘算法的快速实现
3. Permutation-test-based clustering method for detection of dynamic patterns in Spatio-temporal datasets [J] . Liu Qiliang, Liu Wenkai, Tang Jianbo, Computers，environment and urban systems . 2019,第MAY期

机译：基于置换测试的聚类方法在时空数据集中的动态模式检测
4. Finding similar patterns in time stamped temporal datasets [C] . Shadi. A. Aljawarneh, V. Radha Krishna, Aravind Cheruvu International Conference on Engineering MIS . 2017

机译：在时间戳的时间数据集中找到类似的模式
5. Finding spatio-temporal patterns in large sensor datasets. [D] . McGuire, Michael Patrick. 2010

机译：在大型传感器数据集中查找时空模式。
6. An efficient method for mining cross-timepoint gene regulation sequential patterns from time course gene expression datasets [O] . Chun-Pei Cheng, Yu-Cheng Liu, Yi-Lin Tsai, 2013

机译：从时程基因表达数据集中挖掘跨时间点基因调控序列模式的有效方法
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。

Finding similar patterns in time stamped temporal datasets

摘要

著录项

相似文献

相关主题

期刊订阅