Breaking the Deadlock: Simultaneously Discovering Attribute Matching and Cluster Matching with Multi-Objective Metaheuristics.
Breaking the Deadlock: Simultaneously Discovering Attribute Matching and Cluster Matching with Multi-Objective Metaheuristics.
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DOI：
10.1007/s13740-012-0010-0
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发表时间：
2012-08-01
影响因子：
--
通讯作者：
Wang, Hao
中科院分区：
文献类型：
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作者：
Liu, Haishan;Dou, Dejing;Wang, Hao
在本文中，我们提出了一种数据挖掘方法来解决异构数据集匹配的挑战。特别是，我们提出了两个问题的解决方案，在整合信息从不同的科研成果。第一个问题，属性匹配，涉及发现不同的数字特征（属性）之间的对应关系，这些特征用于表征在不同研究实验室中收集和分析的数据集。第二个问题，聚类匹配，涉及发现跨数据集的模式（聚类）之间的匹配。我们把这两个问题一起作为一个多目标优化问题。提出了一种求解最优解的多目标元分析算法，并与遗传算法进行了比较。这种方法的效用证明了一系列的实验，使用合成和现实的数据集，旨在模拟来自不同来源的异构数据。
In this paper, we present a data mining approach to address challenges in the matching of heterogeneous datasets. In particular, we propose solutions to two problems that arise in integrating information from different results of scientific research. The first problem, attribute matching, involves discovery of correspondences among distinct numeric features (attributes) that are used to characterize datasets that have been collected and analyzed in different research labs. The second problem, cluster matching, involves discovery of matchings between patterns (clusters) across datasets. We treat both of these problems together as a multi-objective optimization problem. A multi-objective metaheuristics algorithm is described to find the optimal solution and compared with the genetic algorithm. The utility of this approach is demonstrated in a series of experiments using synthetic and realistic datasets that are designed to simulate heterogeneous data from different sources.