CASE: A clustering algorithm for cell formation with sequence data

G. Jayakrishnan Nair; T.T. Narendran

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CASE: A clustering algorithm for cell formation with sequence data

机译：案例：使用序列数据进行细胞形成的聚类算法

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Cellular manufacturing is a well-known strategy for reducing lead times in batch production systems. Most of the methods of cell formation are based on machine- component incidence alone. However, other factors such as production sequence and product volumes, if incorporated, can enhance the quality of the solutions. This study uses sequence data for cell-formation. A new similarity measure is Defined for this purpose and appropriate measures of performance for evaluating Solutions are introduced. A clustering approach of the non-hierarchical type Is proposed. With new seeding techniques, the proposed algorithm clusters Machines and components on the basis of sequence data. The algorithm gives Encouraging results when applied to sample problems.

机译：蜂窝制造是减少批量生产系统中交货时间的众所周知的策略。大多数细胞形成方法仅基于机器零件的发生率。但是，如果结合了其他因素，例如生产顺序和产品数量，则可以提高解决方案的质量。这项研究使用序列数据进行细胞形成。为此，定义了一个新的相似性度量，并引入了用于评估解决方案的适当性能度量。提出了一种非分层类型的聚类方法。利用新的播种技术，提出的算法基于序列数据对机器和组件进行聚类。当应用于样本问题时，该算法会给出令人鼓舞的结果。

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来源
《International Journal of Production Research》 |1998年第1期|p.157-179|共23页
作者
G. Jayakrishnan Nair; T.T. Narendran;
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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类管理学;
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1. An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data [J] . Abraham P. George, Chandrasekharan Rajendran, Soumyadip Ghosh The International Journal of Advanced Manufacturing Technology . 2003,第1a2期

机译：具有序数级和比率级数据的细胞制造系统中细胞形成的解析迭代聚类算法
2. A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA [J] . SEUNG-JOON OH, JAE-YEARN KIM International Journal of Information Technology & Decision Making . 2005,第1期

机译：基于序列元素的分类序列数据聚类算法
3. Inconsistent Denoising and Clustering Algorithms for Amplicon Sequence Data [J] . Koskinen Kaisa, Auvinen Petri, Bjorkroth K. Johanna, Journal of computational biology: A journal of computational molecular cell biology . 2015,第8期

机译：扩增子序列数据的去噪和聚类算法不一致
4. Fuzzy Clustering-Based Optimised Cell Formation Algorithm Considering Sequence of Operations, Alternative Routing and Part-Volume [C] . Susanto Sani, Bhattacharya Arijit, Al-Dabass David 2010 12th International Conference on Computer Modelling and Simulation (UKSim2010) . 2010

机译：考虑操作顺序，替代路由和部分体积的基于模糊聚类的优化细胞形成算法
5. Efficient Sequence Clustering and Embedding Algorithms for Large-scale Metagenomics Data [D] . Zheng, Wei. 2019

机译：大规模偏心组织数据的高效序列聚类和嵌入算法
6. EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant [O] . Frederic von Wegner, Paul Knaut, Helmut Laufs 2018

机译：不同聚类算法的脑电微状态序列在信息理论上是不变的
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。

CASE: A clustering algorithm for cell formation with sequence data

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