Time-efficient Significance Measure for Discovering Spatiotemporal Co-occurrences from Data with Unbalanced Characteristics

被引：6

作者：

Aydin, Berkay ^{[1
]}

Akkineni, Vijay ^{[1
]}

Angryk, Rafal ^{[1
]}

机构：

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

来源：

23RD ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2015) | 2015年

基金：

美国国家科学基金会;

关键词：

Spatiotemporal co-occurrence pattern; interestingness measure; spatiotemporal knowledge discovery; DATA SETS; PATTERNS;

D O I：

10.1145/2820783.2820871

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Mining spatiotemporal co-occurrence patterns requires assessing the strength of co-occurrences among the instances of different feature types. Currently, a spatiotemporal version of the Jaccard measure is used for measuring the strength of spatiotemporal co-occurrences. We present an extended spatiotemporal version of the Jaccard measure (J*) that is more relevant and efficient for the task of STCOP mining. We also demonstrate the space and time efficiency of the J* with experimental evaluation.

引用

页数：4

共 7 条

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