Statistical spatial clustering using spatial data mining

2008 ◽  
Author(s):  
S. Thirumurugan ◽  
L. Suresh
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

scholarly journals Knowledge Discovery Web Service for Spatial Data Infrastructures

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Morteza Omidipoor ◽  
Ara Toomanian ◽  
Najmeh Neysani Samany ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Web Service ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
High Level

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.

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.

scholarly journals Utilizing MapReduce to Improve Probe-Car Track Data Mining

2018 ◽  
Vol 7 (7) ◽  
pp. 287 ◽  
Author(s):  
Li Zheng ◽  
Meng Sun ◽  
Yuejun Luo ◽  
Xiangbo Song ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Traffic Safety ◽  
Spatial Data ◽  
Road Traffic ◽  
Spatial Data Mining ◽  
Tracking Data ◽  
Mining System ◽  
Computational Performance ◽  
True Path ◽  
Probe Car

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

scholarly journals SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING

2011 ◽  
Vol 5 (3) ◽  
pp. 52
Author(s):  
Atje Setiawan ◽  
Rudi Rosadi
Keyword(s):  
Data Mining ◽  
Elementary School ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Education Quality ◽  
Percentage Error ◽  
Kriging Method ◽  
National Education ◽  
Software Application ◽  
Average Percentage

The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province.  The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords—  Expansion SAR, SAR-Kriging, quality education

Spatial data mining to support pandemic preparedness

2006 ◽  
Vol 8 (1) ◽  
pp. 80-82 ◽  
Author(s):  
Chris Bailey-Kellogg ◽  
Naren Ramakrishnan ◽  
Madhav V. Marathe
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Pandemic Preparedness
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