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首页> 外文会议>8th World Multi-Conference on Systemics, Cybernetics and Informatics(SCI 2004) vol.2: Computing Techniques >Heuristics and Meta-heuristics for one-way clustering of gene expression data
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Heuristics and Meta-heuristics for one-way clustering of gene expression data

机译：基因表达数据单向聚类的启发式和元启发式

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摘要

One-way gene expression clustering problem consists of identifying or grouping/partitioning characteristic vectors of all related genes. Application of crossing minimization heuristics with recursive noise removal in the domain of unsupervised clustering of one-way gene expression data is a novel idea, presented first by [1]. In this paper, we have further explored this idea through an objective comparison of five crossing minimization heuristics along with two Meta combinations. We have established, after performing extensive computational experiments that for weak clusters even under a noiseless environment the performance of Median Heuristic of [2] drastically deteriorates thus making recursive noise removal ineffective, however MaxSort heuristic of [3] and its derivative gives near perfect results but deteriorates when noise is added. Surprisingly the performance of meta-heuristic i.e. MaxSort followed by Median Heuristic results in less overall time and significantly better overall clustering results. Lastly we apply the heuristics and meta-heuristics on real data and objectively compare the outputs with promising results.
机译：单向基因表达聚类问题包括对所有相关基因的特征载体进行识别或分组/划分。交叉最小化启发式算法与递归噪声消除技术在单向基因表达数据的无监督聚类领域中的应用是一个新颖的想法，首先由[1]提出。在本文中，我们通过对五个交叉最小化启发式方法以及两个Meta组合的客观比较，进一步探索了这个想法。经过广泛的计算实验，我们已经建立了即使对于弱集群，即使在无噪声的环境下，[2]的中值启发式方法的性能也会急剧下降，从而使递归噪声消除无效，但是[3]的MaxSort启发式及其派生给出的结果几乎完美。但是当添加噪音时会恶化。令人惊讶的是，元启发式算法（即MaxSort和随后的中值启发式算法）的性能可减少总时间，并显着改善总体聚类结果。最后，我们将启发式方法和元启发式方法应用于实际数据，并客观地比较输出结果和有希望的结果。

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