发现微阵列数据中双簇的混合多目标粒子群优化方法
A Hybrid Multi Objective Particle Swarm Optimization Method to Discover Biclusters in Microarray Data
摘要：
近年来，随着微阵列技术的发展，从微阵列数据中发现有用的知识变得非常重要。双聚类是一种非常有用的数据采掘技术，用于发现具有相似行为的基因。在微阵列数据中，必须同时优化多个目标，并且这些目标经常相互冲突。多目标模型能够解决这样的问题。提出了一种基于多目标粒子群优化的混合算法，用于发现基因表达数据中的双聚类。在我们的方法中，我们将考虑双聚类之间的低水平重叠，并尝试覆盖基因表达矩阵的所有元素。在基准数据库中的实验结果表明，在双聚类之间的重叠和基因表达矩阵中的元素的覆盖率显着改善。
英文摘要：
In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.
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