CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data-猫眼课题宝

DOI：

10.1145/3474717.3483989

发表时间：

2021-11

期刊：

Proceedings of the 29th International Conference on Advances in Geographic Information Systems

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Xu Teng;Thomas Beckler;Bradley Gannon;Benjamin Huinker;Gabriel Huinker;Koushhik Kumar;Christina Marquez;Jacob Spooner;Goce Trajcevski;Prabin Giri;A. Dotter;J. Andrews;S. Coughlin;Y. Qin;J. G. Serra-Perez;N. Tran;Jaime Roman-Garja;K. Kovlakas;E. Zapartas;S. Bavera;D. Misra;T. Fragos

传统上，多变量数据聚类的目的是基于合适的相似性（反之，距离）函数对具有多个异构属性的对象进行分组。其中一个主要的挑战是，它不是直接应用数学运算（例如，求和，平均）到特征值，因为它们来自异构上下文。在这项工作中，我们将挑战进一步推进，并解决了基于联合考虑的多变量数据集聚类问题：(a)属性子集之间的相似性；(b)属性的另一个子集之间基于距离的多样性。具体来说，我们专注于天体物理学数据，其中不同恒星的恒星演化快照包含40多个不同的属性，对应于各种物理和分类（例如，“黑洞”）属性。我们提出了CSD-CAMD——一个用于天体物理多变量数据集聚类的相似性和多样性耦合的原型系统。它为用户选择他们喜欢的属性子集提供了灵活性；分配权重（以反映它们在聚类中的相对重要性）；选择影响应该是在距离上还是在距离上。此外，CSD-CAMD允许选择聚类算法，并支持聚类结果的可视化。

Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts. In this work we take the challenge a step further and tackle the problem of clustering multivariate datasets based on jointly considering: (a) similarity among a subset of the attributes; and (b) distance-based diversity among another subset of the attributes. Specifically, we focus on astrophysics data, where the snapshots of the stellar evolution for different stars contain over 40 distinct attributes corresponding to various physical and categorical (e.g., 'black hole') attributes. We present CSD-CAMD -- a prototype system for Coupling Similarity and Diversity for Clustering Astrophysics Multivariate Datasets. It provides a flexibility for the users to select their preferred subsets of attributes; assign weight (to reflect their relative importance on the clustering); and select whether the impact should be in terms of proximity or distance. In addition, CSD-CAMD allows for selecting a clustring algorithm and enables visualization of the outcome of clustering.

CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data

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