scholarly journals An Efficient Grid Cell Based Spatial Clustering Algorithm for Spatial Data Mining

2003 ◽  
Vol 10D (4) ◽  
pp. 567-576
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Grid Cell ◽  
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 Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform

2017 ◽  
Vol 9 (12) ◽  
pp. 1301 ◽  
Author(s):  
Fang Huang ◽  
Qiang Zhu ◽  
Ji Zhou ◽  
Jian Tao ◽  
Xiaocheng Zhou ◽  
...  
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Data Mining ◽  
Dbscan Clustering

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 Prediction of Suitability of Soil for Different Crops using Spatial Data Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 2330-2337 ◽  
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Geographical Location ◽  
Spatial Data Mining ◽  
Soil Conditions ◽  
Data Sets ◽  
Grid Pattern ◽  
Multiple Data Sets

The main Objective of Data mining in agriculture is to improvise the productivity based on the data observed and timelines of cultivation. Spatial Data mining, a key to capture the data by proposing sensors on a particular geographical location and observe various parameters to enhance the productivity based on the statistical analysis of data collected. In general, Data mining is an anticipating measurement and prognosticates the various data sets and mutate into useful data sets which can be applied on various applications. In this paper, data mining is applied in bridging the soil conditions to the applicable crop for cultivation in enhancing the productivity and multiple crops cultivation for enriched productivity based on the data sets acquired. A Statistical analysis resulted from a backend algorithm with the data sets and displayed as dashboard with the forecasted productivity. A Grid based clustering algorithm is adhered at the backend for performing analysis on the collected data sets results crop selectivity & productivity timelines. Geographical analysis forms a grid pattern with multiple data sets as matrix results in multiple crop selectivity based on the soil conditions and analyzed data sets obtained from various sensor parameters on a particular location. Data visualization is performed after the algorithmic process at the backend and data stored in the cloud server. Spatial Survey & Collective data Sets analyzed with the algorithm are used to elevate the Crop Selectivity and productivity on a soil based on the Biological Predicts, defoliant and manure usage timelines yields Improved Monetary generation.

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