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Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

机译：基于GKPFCM聚类算法的典型性，通过典型数据集和彩色图像中的模式识别

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B. Ojeda-Magaña; R. Ruelas; M. A. Corona Nakamura; D. W. Carr Finch; L. Gómez-Barba;
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年度 2013
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1. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm [J] . Ojeda-Magana B., Ruelas R., Corona Nakamura M. A., Mathematical Problems in Engineering . 2013,第pta16期

机译：基于GKPFCM聚类算法的典型数值数据集和彩色图像模式识别。
2. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm [J] . B.Ojeda-Maga?a, R.Ruelas, M. A.Corona Nakamura, Mathematical Problems in Engineering: Theory, Methods and Applications . 2013,第2期

机译：基于GKPFCM聚类算法的典型数值数据集和彩色图像模式识别。
3. A GREATER KNOWLEDGE EXTRACTION CODED AS FUZZY RULES AND BASED ON THE FUZZY AND TYPICALITY DEGREES OF THE GKPFCM CLUSTERING ALGORITHM [J] . B. Ojeda-Magana, R. Ruelas, F. S. Buendia-Buendia, Intelligent automation and soft computing . 2009,第4期

机译：基于GKPFCM聚类算法的模糊度和典型度，编码为模糊规则的更大的知识提取
4. Soft Computing Approach to Pattern Recognition and Image Processing - Part I: Pattern Recognition - Clustering Large Data Sets [C] . M. Narasimha Murty CIMPA-UNESCO-INDIA School on Soft Computing Approach to Pattern Recognition and Image Processing . 2002

机译：模式识别和图像处理的软计算方法 - 第I部分：模式识别 - 群集大数据集
5. Unsupervised Data-Driven Algorithms with Adaptive Metric for Pattern Recognition, Hypothesis Testing, and Image Segmentation [D] . Guan, Jiahui. 2018

机译：具有自适应度量的无监督数据驱动算法，用于模式识别，假设检测和图像分割
6. Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns [O] . Shaoming Pan, Yongkai Li, Zhengquan Xu, -1

机译：基于数据访问模式的地理空间图像数据分布式存储算法
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。
8. A Color Stereo Image Set for Evaluation of Ordnance Recognition Algorithms [R] . Olson, C. 1998

机译：一种用于评估军械识别算法的彩色立体图像集

Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

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