Fast mining of spatial frequent wordset from social database

Lee, Yongmi; Nam, Kwang Woo; Ryu, Keun Ho

doi:10.1007/s41324-017-0094-6

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…The BIRCH algorithm uses a tree structure to help us cluster quickly [30]. A density-based algorithm contains two parameters (eps and minPts) to identify dense regions based on density reachability [31,32]. Density clustering algorithms are suitable for clusters of arbitrary shapes in geo-tagged photos and videos with FoVs [4,[33][34][35].…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Discovering Homogeneous Groups from Geo-Tagged Videos

Lew

Nam

2023

Sensors

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The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.

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Section: Clustering Algorithmsmentioning

confidence: 99%

Discovering Homogeneous Groups from Geo-Tagged Videos

Lew

Nam

2023

Sensors

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“…This approach consists of four algorithms, and FP-Growth algorithm is utilized as one method to find colocation by finding all frequent itemsets. Lee et al [23] proposed SFP-Growth algorithms to find spatial frequent patterns from social data. This study divides the entire space into cells on a 2D grid and manages cells hierarchically by dividing the side of a cell by four.…”

Section: Fp-growth Based On Spatial Datamentioning

confidence: 99%

FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

et al. 2021

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With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

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“…Detecting the content of POIs was begun by analyzing the geo-tagged text of microblog data [24,25]. However, geo-tagged photos contain much more content than textual information.…”

Section: Mining Geo-tagged Photosmentioning

confidence: 99%

DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

Park

Lew

Nam

et al. 2021

IJGI

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Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm.

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Fast mining of spatial frequent wordset from social database

Cited by 5 publications

References 18 publications

Discovering Homogeneous Groups from Geo-Tagged Videos

Discovering Homogeneous Groups from Geo-Tagged Videos

FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

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