An Externally-Constrained Ising Clustering Method for Material Informatics
An Externally-Constrained Ising Clustering Method for Material Informatics
复制标题
材料信息学的外部约束 Ising 聚类方法
DOI：
10.1109/candarw53999.2021.00040
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kobayashi Hiroaki
中科院分区：
文献类型：
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作者：
Komatsu Kazuhiko;Kumagai Masahito;Qi Ji;Sato Masayuki;Kobayashi Hiroaki
由于数据科学的最新进展，如机器学习和大数据分析，使用数据科学技术的方法甚至吸引了人们的注意力，以开发新材料，称为材料信息学。在材料信息学中，聚类是理解热物理性质的基本数据处理技术之一。因此，聚类质量是要考虑的高优先级。为了提高聚类精度，本文评估基于伊辛的聚类方法使用退火机。由于退火机使Ising模型的能量最小化，基于Ising的聚类方法将聚类定义为Ising模型，以最小化数据之间的簇内距离之和。由于材料信息学中常用的非Ising基聚类方法是伪优化的，因此Ising基聚类方法可以获得较高的聚类精度。实验结果表明，与传统的K-means聚类方法相比，基于Ising的外部约束聚类方法在合理的执行时间内获得了更高的聚类精度.
Due to the recent advancement of data science, such as machine learning and big-data analysis, the approach using data science techniques has attracted attention even to develop new materials, called material informatics. In material informatics, clustering is one of the essential data processing techniques to understand thermophysical properties. Thus, clustering quality is a high priority to be considered. To improve clustering accuracy, this paper evaluates Ising-based clustering methods using an annealing machine. As an annealing machine minimizes the energy of an Ising model, the Ising-based clustering methods define the clustering as an Ising model to minimize the sum of intra-cluster distances among data. Since the non-Ising-based clustering methods conventionally used in materials informatics perform pseudo-optimization, the Ising-based clustering methods can achieve high clustering accuracy. The experimental results show that the Ising-based clustering method with externally-defined constraint achieves higher clustering accuracy with an affordable execution time than the conventional K-means clustering method.