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.

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.

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

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.

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.

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