scholarly journals Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship

2021 ◽  
Vol 18 (6) ◽  
pp. 8223-8244
Author(s):  
Meijiao Wang ◽  
◽  
Yu chen ◽  
Yunyun Wu ◽  
Libo He
Keyword(s):  
Pattern Mining ◽  
Local Density ◽  
Time And Space ◽  
Density Peak ◽  
Location Pattern ◽  
Geographic Space ◽  
Location Patterns ◽  
Density Peak Clustering ◽  
Neighbor Relationship ◽  
Relationship Of

<abstract> <p>Spatial co-location pattern mining discovers the subsets of spatial features frequently observed together in nearby geographic space. To reduce time and space consumption in checking the clique relationship of row instances of the traditional co-location pattern mining methods, the existing work adopted density peak clustering to materialize the neighbor relationship between instances instead of judging the neighbor relationship by a specific distance threshold. This approach had two drawbacks: first, there was no consideration in the fuzziness of the distance between the center and other instances when calculating the local density; second, forcing an instance to be divided into each cluster resulted in a lack of accuracy in fuzzy participation index calculations. To solve the above problems, three improvement strategies are proposed for the density peak clustering in the co-location pattern mining in this paper. Then a new prevalence measurement of co-location pattern is put forward. Next, we design the spatial co-location pattern mining algorithm based on the improved density peak clustering and the fuzzy neighbor relationship. Many experiments are executed on the synthetic and real datasets. The experimental results show that, compared to the existing method, the proposed algorithm is more effective, and can significantly save the time and space complexity in the phase of generating prevalent co-location patterns.</p> </abstract>

scholarly journals Mining spatial dynamic co-location patterns

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1491-1497 ◽  
Author(s):  
Jiangli Duan ◽  
Wang Lizhen ◽  
Xin Hu ◽  
Hongmei Chen
Keyword(s):  
Spatial Data ◽  
Ganoderma Lucidum ◽  
Pattern Mining ◽  
Spatial Data Mining ◽  
Spatial Dynamic ◽  
Location Pattern ◽  
Geographic Space ◽  
Spatial Features ◽  
Location Patterns ◽  
Dynamic Relationships

Spatial co-location pattern mining is an important part of spatial data mining, and its purpose is to discover the coexistence spatial feature sets whose instances are frequently located together in a geographic space. So far, many algorithms of mining spatial co-location pattern and their corresponding expansions have been proposed. However, dynamic co-location patterns have not received attention such as the real meaningful pattern {Ganoderma lucidum new, maple tree dead} means that ?Ganoderma lucidum? grows on the ?maple tree? which was already dead. Therefore, in this paper, we propose the concept of spatial dynamic co-location pattern that can reflect the dynamic relationships among spatial features and then propose an algorithm of mining these patterns from the dynamic dataset of spatial new/dead features. Finally, we conduct extensive experiments and the experimental results demonstrate that spatial dynamic co-location patterns are valuable and our algorithm is effective.

scholarly journals Spatial Co-Location Pattern Discovery from Fuzzy Objects

2017 ◽  
Vol 26 (02) ◽  
pp. 1750003 ◽  
Author(s):  
Zhiping Ouyang ◽  
Lizhen Wang ◽  
Pingping Wu
Keyword(s):  
Pattern Mining ◽  
Search Space ◽  
Pattern Generation ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Location Pattern ◽  
Biomedical Image ◽  
The Past ◽  
Location Patterns ◽  
Fuzzy Objects

A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern mining (RCP), to mining co-location patterns at a membership threshold or within a membership range. For efficient SCP mining, we optimize the basic mining algorithm to accelerate the co-location pattern generation. To improve the performance of RCP mining, effective pruning strategies are developed to significantly reduce the search space. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments.

scholarly journals ON THE RELATIONSHIPS BETWEEN CLUSTERING AND SPATIAL CO-LOCATION PATTERN MINING

2008 ◽  
Vol 17 (01) ◽  
pp. 55-70 ◽  
Author(s):  
YAN HUANG ◽  
PUSHENG ZHANG ◽  
CHENGYANG ZHANG
Keyword(s):  
Pattern Mining ◽  
Spatial Clustering ◽  
Spatial Proximity ◽  
Location Pattern ◽  
Continuous Space ◽  
Clustering Techniques ◽  
Spatial Objects ◽  
Spatial Features ◽  
Statistical Measures ◽  
Location Patterns

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping co-location mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined co-location patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets.

scholarly journals THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 763-767
Author(s):  
Z. W. Liu ◽  
B. Wei ◽  
C. L. Kang ◽  
J. W. Jiang
Keyword(s):  
Spatial Data ◽  
Pattern Mining ◽  
Score Function ◽  
Participation Rate ◽  
Spatial Data Mining ◽  
Hesitant Fuzzy Set ◽  
Location Pattern ◽  
Mining Algorithm ◽  
Location Patterns ◽  
Definition Of

Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.

scholarly journals MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS

2018 ◽  
Vol XLII-3 ◽  
pp. 2505-2509
Author(s):  
G. Zhou ◽  
Q. Li ◽  
G. Deng ◽  
T. Yue ◽  
X. Zhou
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Location Pattern ◽  
Spatial Feature ◽  
Geographic Space ◽  
Location Patterns ◽  
Spatial Data Sets

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

scholarly journals Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2014
Author(s):  
Yi Lv ◽  
Mandan Liu ◽  
Yue Xiang
Keyword(s):  
Prior Knowledge ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Density Peak ◽  
Adaptive Clustering ◽  
Clustering Center ◽  
Density Peak Clustering ◽  
Shared Nearest Neighbor ◽  
Fast Searching

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.

scholarly journals Accelerating Density Peak Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 859 ◽  
Author(s):  
Lin
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Early Stage ◽  
Separation Distance ◽  
Density Peak ◽  
Density Peaks ◽  
Density Based Clustering ◽  
Data Points ◽  
Data Point ◽  
Density Peak Clustering

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm.

scholarly journals Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

2020 ◽  
Vol 49 (3) ◽  
pp. 395-411
Author(s):  
Qiannan Wu ◽  
Qianqian Zhang ◽  
Ruizhi Sun ◽  
Li Li ◽  
Huiyu Mu ◽  
...  
Keyword(s):  
High Dimension ◽  
Clustering Algorithm ◽  
Local Density ◽  
Nearest Neighbors ◽  
Allocation Strategy ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Density Peak ◽  
Real World Datasets ◽  
Density Peak Clustering

Cluster analysis plays a crucial component in consumer behavior segment. The density peak clustering algorithm (DPC) is a novel density-based clustering method. However, it performs poorly in high-dimension datasets and the local density for boundary points. In addition, its fault tolerance is affected by one-step allocation strategy. To overcome these disadvantages, an adaptive density peak clustering algorithm based on dimensional-free and reverse k-nearest neighbors (ERK-DPC) is proposed in this paper. First, we compute Euler cosine distance to obtain the similarity of sample points in high-dimension datasets. Then, the adaptive local density formula is used to measure the local density of each point. Finally, the reverse k-nearest neighbor idea is added on two-step allocation strategy, which assigns the remaining points accurately and effectively. The proposed clustering algorithm is experiments on several benchmark datasets and real-world datasets. By comparing the benchmarks, the results demonstrate that the ERK-DPC algorithm superior to some state-of- the-art methods.

scholarly journals The Coupling Co-Location Pattern: A New Spatial Pattern for Spatial Data Sets

2020 ◽  
Author(s):  
Shiran Zhou ◽  
Lizhen Wang ◽  
Pingping Wu
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Pattern Mining ◽  
Mutual Influence ◽  
Practical Significance ◽  
Data Sets ◽  
Location Pattern ◽  
Spatial Features ◽  
Location Patterns ◽  
Spatial Data Sets

There is a variety of interesting knowledge in spatial data sets. Spatial co-location pattern mining can discover sets of different features that are co-located. However, this type of pattern only lists the features that appear together without any consideration of the quantity ratio, which can cause confusion. For example, the co-location pattern church, restaurants shows that churches and restaurants are often close to each other, but information such as how many restaurants are near a church is usually not displayed. Also, in real spatial data sets, there is a mutual influence between spatial features, that is, a coupling relationship between different features or the same features. Thus, this paper proposes a novel spatial pattern called a coupling co-location pattern. First, we discuss the properties of the coupling phenomenon between spatial features, and then the concept of coupling co-location patterns is defined formally. Second, the measurement of support and mining framework for coupling co-location patterns are proposed. Finally, we conduct experiments on both real and synthetic data sets, and the results verify the practical significance of coupling co-location patterns.

Sign in / Sign up

Export Citation Format

Share Document