Optimizing parameters in fuzzy k-means for clustering microarray data.

机译：在模糊k均值中优化参数以对微阵列数据进行聚类。

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Rapid advances of microarray technologies are making it possible to analyze and manipulate large amounts of gene expression data. Clustering algorithms, such as hierarchical clustering, self-organizing maps, k-means clustering and fuzzy k-means clustering, have become important tools for expression analysis of microarray data. However, the need of prior knowledge of the number of clusters, k, and the fuzziness parameter, b, limits the usage of fuzzy clustering. Few approaches have been proposed for assigning best possible values for such parameters.; In this thesis, we use simulated annealing and fuzzy k-means clustering to determine the optimal parameters, namely the number of clusters, k, and the fuzziness parameter, b. To assess the performance of our method, we have used synthetic and real gene experiment data sets.; To improve our approach, two methods, searching with Tabu List and Shrinking the scope of randomization, are applied. Our results show that a nearly-optimal pair of k and b can be obtained without exploring the entire search space.

机译：微阵列技术的飞速发展使得分析和操纵大量基因表达数据成为可能。聚类算法，例如层次聚类，自组织图，k均值聚类和模糊k均值聚类，已成为微阵列数据表达分析的重要工具。但是，对簇数k和模糊性参数b的先验知识的需求限制了模糊聚类的使用。很少有人提出为这种参数分配最佳可能值的方法。在本文中，我们使用模拟退火和模糊k均值聚类来确定最佳参数，即聚类数k和模糊性参数b。为了评估我们方法的性能，我们使用了合成和真实基因实验数据集。为了改进我们的方法，应用了两种方法，即使用禁忌列表搜索和缩小随机范围。我们的结果表明，无需探索整个搜索空间即可获得近乎最优的k和b对。

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作者
Yang, Wei.;
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作者单位

University of Windsor (Canada).;

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授予单位 University of Windsor (Canada).;
学科 Computer Science.
学位 M.Sc.
年度 2005
页码 96 p.
总页数 96
原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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专利

1. On Finding the Best Parameters of Fuzzy k-Means for Clustering Microarray Data [J] . Wei Yang, Luis Rueda, Alioune Ngom Journal of multiple-valued logic and soft computing . 2007,第1a2期

机译：寻找用于聚类芯片数据的模糊k均值的最佳参数
2. Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset [J] . G. Surya Narayana, Kamakshaiah Kolli Multimedia Tools and Applications . 2021,第3期

机译：具有大型数据集的传播多式化优化集群的多变核估计快速密度峰值聚类的模糊k均值聚类
3. Fuzzy C-means method for clustering microarray data. [J] . Dembele D, Kastner P Bioinformatics . 2003,第8期

机译：芯片数据聚类的模糊C-均值方法。
4. Adaptive Predictor with Dynamic Fuzzy K-Means Clustering for Lossless Image CodingAdaptive Predictor with Dynamic Fuzzy K-Means Clustering for Lossless Image CodingAdaptive Predictor with Dynamic Fuzzy K-Means Clustering for Lossless Image CodingAdap [C] . Lih-Jen Kau IEEE International Conference on Fuzzy Systems . 2003

机译：具有动态模糊K-MERIAL的自适应预测因子用于无损图像编码预测因子，动态模糊K-MERIAL用于无损图像编码预测因子，具有动态模糊K-MERION GORSLEST Image CodingAP的群
5. K-means clustering with automatic determination of K using a Multiobjective Genetic Algorithm with applications to microarray gene expression data. [D] . Shaw, Matthew Karl Ellis. 2015

机译：使用多目标遗传算法自动确定K值的K均值聚类，并应用于微阵列基因表达数据。
6. Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm [O] . Liang Chen, Weinan Wang, Yuyao Zhai, 2020

机译：基于多项式建模和自适应模糊K均值算法的单细胞转录组数据聚类
7. Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting [O] . M.Shochibul Burhan, Wayan Firdaus Mahmudy, Rizdania Dermawi 2018

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Optimizing parameters in fuzzy k-means for clustering microarray data.

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