Bounding Box Representation of Co-location Instances for L∞ Induced Distance Measure

Witold Andrzejewski; Paweł Boiński

doi:10.1007/978-3-030-86534-4_1

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Title

Bounding Box Representation of Co-location Instances for L∞ Induced Distance Measure

Authors

Witold Andrzejewski (WIiT) ^{[ 1 ][ 2.3 ][ P ]}
Paweł Boiński (WIiT) ^{[ 1 ][ 2.3 ][ P ]}

^{[ 1 ]} Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2021

Chapter type

chapter in monograph / paper

Publication language

english

Keywords

EN

co-location
bounding box
data mining

Abstract

EN In this paper, we investigate the efficiency of Co-location Pattern Mining (CPM). In popular methods for CPM, the most time-consuming step consists of identifying of pattern instances, which are required to calculate the potential interestingness of the pattern. We tackle this problem and provide an instance identification method that has lower complexity than the state-of-the-art approach: (1) we introduce a new representation of co-location instances based on bounding boxes, (2) we formulate and prove several theorems regarding such a representation that can improve instances identification step, (3) we provide a novel algorithm utilizing the aforementioned theorems and analyze its complexity. Finally, we experimentally demonstrate the efficiency of the proposed solution.

Date of online publication

05.09.2021

Pages (from - to)

3 - 14

DOI

10.1007/978-3-030-86534-4_1

URL

https://link.springer.com/chapter/10.1007/978-3-030-86534-4_1

Book

Big Data Analytics and Knowledge Discovery : 23rd International Conference, DaWaK 2021, Virtual Event, September 27–30, 2021, Proceedings

Presented on

23rd International Conference on Big Data Analytics and Knowledge Discovery DaWaK 2021, 27-30.09.2021