Recent Trends in Spatial Data Mining and Its Challenges

2016 ◽  
pp. 37-54
Author(s):  
Anuradha Jagadeesan ◽  
Prathik A ◽  
Tripathy B K
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Spatial Data ◽  
Science And Technology ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Research Field ◽  
Spatial Characteristics ◽  
Human Ability ◽  
Recent Trends

With tremendous development in the field of science and technology, there is vast amount of data which are used in analytics for decision making. Considering its spatial characteristics for mining will enhance the accuracy of decision. So, obtaining knowledge from spatial data becomes very essential and meaningful. The spatial database contains very numerous amounts of spatial and non-spatial data of different forms. Interpretation and analyzing of vast data is far beyond human ability. In order to acquire knowledge on such scenario we need spatial data mining. The challenges involved in spatial mining are to deal with different objects that represent the spatial characteristics. This makes spatial data mining a dominant research field. This chapter briefs about the characteristics of spatial data mining and the methods of spatial data mining in recent years.

The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

Geographic Knowledge Discovery in Multiple Spatial Databases

2017 ◽  
pp. 344-366 ◽  
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

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 Possibilities Use to Selected Methods of Spatial Data Mining in Demographic Data Analytics

2018 ◽  
Vol 9 ◽  
pp. 45-55
Author(s):  
Krystyna Kurowska ◽  
Ewa Kietlinska ◽  
Hubert Kryszk
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Spatial Data ◽  
Planning Process ◽  
Demographic Data ◽  
Spatial Data Mining ◽  
High Resolution Data ◽  
Private And Public ◽  
Analyse Data ◽  
Analytical Tools

The main purpose of data mining in private and public sector institutions is to process and analyse data with the aim of generating reliable information for decision-making. Decision-making performance is determined by the availability of the relevant data and the user’s ability to adapt that data for analytical purposes. The popularity of spatial statistical tools is on the rise owing to the complexity of the analysed factors, their variation over time and their correlations with the spatial structure. Popular models should be applied in demographic analyses for the needs of the spatial planning process. The availability of high-resolution data and accurate analytical tools enhances the value of spatial analyses, and the described models can be universally applied to support the decision-making process. The aim of this study was to present the applicability of selected spatial statistical models for analysing demographic data in the planning process and to identify the main advantages of these models.

Geographic Knowledge Discovery in Multiple Spatial Databases

2018 ◽  
pp. 121-143
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

scholarly journals Spatial Data Mining to Support Environmental Management and Decision Making—A Case Study in Brazil

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Carlos Roberto Valêncio ◽  
Fernando Tochio Ichiba ◽  
Guilherme Priólli Daniel ◽  
Rogéria Cristiane Gratão de Souza ◽  
Leandro Alves Neves ◽  
...  
Keyword(s):  
Data Mining ◽  
Environmental Management ◽  
Decision Making ◽  
Spatial Data ◽  
Spatial Data Mining

Spatial Data Mining Based on Campus GIS

2011 ◽  
Vol 282-283 ◽  
pp. 641-645 ◽  
Author(s):  
Yong Ling Yu ◽  
Tao Guan ◽  
Jin Fa Shi
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Prototype System ◽  
Spatial Features ◽  
Database Technology ◽  
Mining Model

With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the campus GIS of a university has shown the feasibility and validity of the system.

The Optimization Algorithm and Applied in Soil Fertility Evaluation Based on Data Mining

2014 ◽  
Vol 644-650 ◽  
pp. 1737-1740
Author(s):  
Li Ma ◽  
Gui Fen Chen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Soil Fertility ◽  
Spatial Data ◽  
Rough Sets ◽  
Evaluation Model ◽  
Attribute Reduction ◽  
Spatial Data Mining ◽  
New Ideas

Clustering, rough sets and decision tree theory were applied to the evaluation of soil fertility levels ,and provided new ideas and methods among the spatial data mining and knowledge discovery. In the experiment, the rough sets - decision tree evaluation model establish by 1400 study samples, the accuracy rate is 92% of the test. The results show :model has good generalization ability; the use of rough sets attribute reduction, can remove redundant attributes, can reduce the size of decision tree decision-making model, reduce the decision-making rules and improving the decision-making accuracy, using the combination of rough set and decision tree decision-making method to infer the level of a large number of unknown samples.

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