Name
Abstract | ||
|---|---|---|
The main purpose of co-location pattern mining is to mine the set of spatial features whose instances are frequently located together in space. Because a single distance threshold is chosen in the previous methods when generating the neighbourhood relationships, some interesting spatial colocation patterns can't be extracted. In addition, previous methods don't take the neighborhood degree into consideration and they depend upon the PI (participation index) to measure the prevalence of the co-locations, which these methods are very sensitive to PI and also lead to the absence of co-location patterns. In order to overcome these limitations of traditional co-location pattern mining, considering that the neighbor relationship is a fuzzy concept, this paper introduces the fuzzy theory into co-location pattern mining, a new fuzzy spatial neighborhood relationship measurement between instances and a reasonable feature proximity measurement between spatial features are proposed. Then, a novel algorithm based on fuzzy C-medoids clustering algorithm, FCB, is proposed, extensive experiments on synthetic and real-world data sets prove the practicability and efficiency of the proposed mining algorithm, it also proves that the algorithm has low sensitivity to thresholds and has high robustness. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICBK.2019.00025 | 2019 IEEE International Conference on Big Knowledge (ICBK) |
Keywords | Field | DocType |
spatial co-location mining,fuzzy set theory,fuzzy neighborhood relationships,fuzzy clustering | Fuzzy concept,Fuzzy clustering,Data mining,Data set,Computer science,Fuzzy logic,Robustness (computer science),Fuzzy set,Neighbourhood (mathematics),Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-7281-4608-9 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Lei | 1 | 1 | 0.35 |
| Lizhen Wang | 2 | 153 | 26.16 |
| Xiaoxuan Wang | 3 | 1 | 0.35 |