Mining spatiotemporal co-occurrence patterns in solar datasets

被引：10

作者：

Aydin, B. ^{[1
]}

Kempton, D. ^{[1
]}

Akkineni, V. ^{[1
]}

Angryk, R. ^{[1
]}

Pillai, K. G. ^{[2
]}

机构：

[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA

[2] Montana State Univ, Dept Comp Sci, Bozeman, MT 59717 USA

来源：

ASTRONOMY AND COMPUTING | 2015年 / 13卷

基金：

美国国家科学基金会;

关键词：

Spatiotemporal co-occurrence; Frequent pattern mining; Solar data mining; Spatiotemporal indexing; DATA SETS; TRAJECTORIES; DISCOVERY;

D O I：

10.1016/j.ascom.2015.10.003

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We address the problem of mining spatiotemporal co-occurrence patterns (STCOPs) in solar datasets with extended polygon-based geometric representations. Specifically designed spatiotemporal indexing techniques are used in the mining of STCOPs. These include versions of two well-known spatiotemporal trajectory indexing techniques: the scalable and efficient trajectory index and Chebyshev polynomial indexing. We present a framework, STCOP-MINER, implementing a filter-and-refine STCOP mining algorithm, with the indexing techniques mentioned for efficiently performing data analysis. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：136 / 144

页数：9