Parallel GPU-based Plane-Sweep Algorithm for Construction of iCPI-Trees

Andrzejewski, Witold; Boinski, Pawel

doi:10.4018/jdm.2015070101

Cited by 18 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [3], the problem studied was to find statistically significant co-location patterns based on hypothesis testing, where some models are assumed which limits its application scope. In [30], [23] and [29], [2], [21], Map Reduce based methods and parallel algorithms on GPU were developed for the co-location pattern mining problem, respectively. In [11], [10], it was studied to find co-location patterns where a set C of spatial labels corresponds to a pattern if the clusterings each based on the objects with a spatial label in C have at least a certain degree of overlap which is captured by the area intersected by the polygons formed based on the clusters.…”

Section: Conclusion On Resultsmentioning

confidence: 99%

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Chan

Long

Yan

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

Co-location patterns are well-established on spatial objects with categorical labels, which capture the phenomenon that objects with certain labels are often located in close geographic proximity. Similar to frequent itemsets, co-location patterns are defined based on a support measure which quantifies the popularity (or prevalence) of a pattern candidate (a label set). Quite a few support measures exist for defining co-location patterns and they share an idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects carry all the labels in C and are located close to one another. Unfortunately, these measures suffer from various weaknesses, e.g., some fail to capture all possible instances while some others overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score whose idea is to count instances fractionally if they overlap. Compared to existing measures, Fraction-Score not only captures all possible instances, but also handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and consistent with the desirable antimonotonicity property). To solve the co-location pattern mining problem based on Fraction-Score, we develop efficient algorithms which are significantly faster than a baseline that adapts the stateof-the-art. We conduct extensive experiments using both real and synthetic datasets, which verified the superiority of Fraction-Score and also the efficiency of our developed algorithms.

show abstract

Section: Conclusion On Resultsmentioning

confidence: 99%

Fraction-Score: A New Support Measure for Co-location Pattern Mining

Chan

Long

Yan

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

show abstract

“…We use both real and synthetic datasets, as shown in Table 3. The first real dataset UK is the set of POIs of the United Kingdom 2 . Each POI has a textual description (e.g., supermarket, bank, cinema) and a GPS location.…”

Section: Experimental Set-upmentioning

confidence: 99%