Spatial clustering methods in data mining

2010 ◽  
pp. 188-217 ◽  
Author(s):  
Jiawei Han ◽  
Micheline Kamber ◽  
Anthony K H Tung
Keyword(s):  
Data Mining ◽  
Spatial Clustering ◽  
Clustering Methods

Spatial Clustering Analysis of K-Means Algorithm in the Classification of Bank Card Customers

2014 ◽  
Vol 687-691 ◽  
pp. 1274-1277
Author(s):  
Kang Lv
Keyword(s):  
Data Mining ◽  
Financial Services ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Spatial Relationship ◽  
Data Bank ◽  
Customer Relationship ◽  
Current Status ◽  
Clustering Methods ◽  
Bank Card

K-means algorithm is a simple and efficient data mining clustering algorithm. For the current status of the bank card customer relationship management, based on data mining technology, design based on K-means clustering algorithm banking customer classification system. Data mining techniques can extract vast amounts of customer information data bank card implicit knowledge and spatial relationship model will represent the bank customers feature set of data objects automatically classified into each composed of clusters of similar objects, bank card customers in the banking system classification. This paper analyzes the existing spatial clustering methods summary and conclusion, based on the combined data bank card customers, according to the volatility of funds used to different customer groups, the use of K-means analysis to study characteristics of client groups, providing appropriate financial services.

scholarly journals Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Arvind Sharma ◽  
R. K. Gupta ◽  
Akhilesh Tiwari
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Objects

There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise). The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

New Combining Rules for Spatial Clustering Methods Using Sigma-Count for Spatial Epidemiology

2020 ◽  
Author(s):  
Laisa Ribeiro de Sa ◽  
Liliane dos Santos Machado ◽  
Jordana de Almeida Nogueira ◽  
Ronei Marcos de Moraes
Keyword(s):  
Spatial Epidemiology ◽  
Spatial Clustering ◽  
Clustering Methods

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Evidence for rapid avoidance of rockfish habitat under reduced quota and comprehensive at-sea monitoring in the British Columbia Pacific halibut fishery

2020 ◽  
Vol 77 (8) ◽  
pp. 1409-1420
Author(s):  
Robyn E. Forrest ◽  
Ian J. Stewart ◽  
Cole C. Monnahan ◽  
Katherine H. Bannar-Martin ◽  
Lisa C. Lacko
Keyword(s):  
British Columbia ◽  
Spatial Clustering ◽  
Clustering Methods ◽  
Quota System ◽  
Encounter Rates ◽  
System A ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Footprint Area ◽  
Management Changes

The British Columbia longline fishery for Pacific halibut (Hippoglossus stenolepis) has experienced important recent management changes, including the introduction of comprehensive electronic catch monitoring on all vessels; an integrated transferable quota system; a reduction in Pacific halibut quotas; and, beginning in 2016, sharp decreases in quota for yelloweye rockfish (Sebastes ruberrimus, an incidentally caught species). We describe this fishery before integration, after integration, and after the yelloweye rockfish quota reduction using spatial clustering methods to define discrete fishing opportunities. We calculate the relative utilization of these fishing opportunities and their overlap with areas with high encounter rates of yelloweye rockfish during each of the three periods. The spatial footprint (area fished) increased before integration, then decreased after integration. Each period showed shifts in utilization among four large fishing areas. Immediately after the reductions in yelloweye rockfish quota, fishing opportunities with high encounter rates of yelloweye rockfish had significantly lower utilization than areas with low encounter rates, implying rapid avoidance behaviour.

scholarly journals Computer-Aided Teaching System Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yonghua Tang ◽  
Qiang Fan ◽  
Peng Liu
Keyword(s):  
Data Mining ◽  
Spectral Clustering ◽  
Traditional Teaching ◽  
Clustering Methods ◽  
Mining System ◽  
Teaching Resources ◽  
Teaching System ◽  
Data Mining Algorithms ◽  
Computer Aided ◽  
Mining Algorithms

The traditional teaching model cannot adapt to the teaching needs of the era of smart teaching. Based on this, this paper combines data mining technology to carry out teaching reforms, constructs a computer-aided system based on data mining, and constructs teaching system functions based on actual conditions. The constructed system can carry out multisubject teaching. Moreover, this paper uses a data mining system to mine teaching resources and uses spectral clustering methods to integrate multiple teaching resources to improve the practicability of data mining algorithms. In addition, this paper combines digital technology to deal with teaching resources. Finally, after building the system, this paper designs experiments to verify the performance of the system. From the research results, it can be seen that the system constructed in this paper has certain teaching and practical effects, and it can be applied to a larger teaching scope in subsequent research.

Abstract MP41: Comparisons of Built Environment Characteristics Inside and Outside of Spatial Clusters of Physical Activity and Obesity in Older U.S. Women

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Kosuke Tamura ◽  
Robin C Puett ◽  
Jaime E Hart ◽  
Heather A Starnes ◽  
Francine Laden ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Spatial Clustering ◽  
Health Study ◽  
Clustering Methods ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Complex Picture ◽  
Intersection Density ◽  
Study Participants

Introduction: Spatial clustering methods have been applied to cancer for over a decade. These methods have been used in studies on physical activity (PA) and obesity. One recent study examined differences in built environment attributes inside and outside PA clusters. We tested two hypotheses: 1) PA and obesity would spatially cluster in older women; and 2) built environment attributes typically related to higher walkability would be found in high PA clusters, while attributes related to lower walkability would appear in high obesity clusters. Methods: We used data from 22,589 Nurses’ Health Study participants (mean age = 69.9 ± 6.8y) in California, Massachusetts, and Pennsylvania. Two outcomes were examined: meeting PA guidelines via self-reported walking (≥ 500 MET-min/week) and obesity (BMI ≥ 30.0). Objective built environment variables were created: population and intersection density, diversity of facilities, and facility density. We used a spatial scan statistic to detect clusters (i.e., areas with high or low rates) of the two outcomes. Built environment attributes were compared inside and outside clusters. Results: Six spatial clusters of PA were found in California and Massachusetts. Two obesity clusters were found in Pennsylvania. Overall there were significant differences (p<0.05) in population and intersection density, and diversity and density of facilities inside and outside clusters. In some cases, built environment attributes related to higher walkability appeared in high PA clusters, while in other PA clusters we did not find this pattern. Differences in built environment attributes inside and outside obesity clusters showed inconsistent patterns. Conclusion: Although PA and obesity clusters emerged, the comparison of built environment attributes inside and outside clusters revealed a complex picture not fully consistent with existing literature. Further examination of PA and obesity clusters in older adults should include other built environment factors that may be related to these outcomes.

Evaluation of Clustering Methods for Adaptive Learning Systems

2015 ◽  
pp. 237-260 ◽  
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

Spatial distribution of Leishmania seropositive dogs in the Angelim neighborhood, Teresina, Piauí, Brazil: appraisal of three spatial clustering methods

GeoJournal ◽  
2020 ◽  
Author(s):  
Lília Aparecida Marques da Silva ◽  
José Ueleres Braga ◽  
João Pereira da Silva ◽  
Maria do Socorro Pires e Cruz ◽  
André Luiz Sá de Oliveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Clustering ◽  
Clustering Methods
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