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The K-neighbor query algorithm is an important class of search algorithm in the spatial database, this paper will adopt the K-means algorithm to carry on sorting to the smallest enclosing rectangle in accordance with orientation relationship based on the measurement of distance and pruning strategies of MBR in the traditional K-nearest neighbor query, it can carry on the K-neighbor queries after sorting,...
In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an...
Spatial data mining (SDM) is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Being an important role of SDM, spatial clustering is to organize a set of spatial objects into groups (or clusters) such that objects in the same group are similar to each other and different from those in other groups. Spatial clustering has been...
This paper studies how to exploit spatial data correlations to group sensor nodes into clusters of high data aggregation efficiency. The approach proposed in this paper first selects a set of cluster heads that form a dominating set. Then a set of nodes selected by ant-colony algorithm are added to the above dominating set to make all the nodes in the set connected. Simulation results demonstrate...
Clustering of spatial data in the presence of obstacles has a wide application. It is an important research topic in the spatial data mining. This paper discusses the problem of spatial clustering with obstacles constraints and presents a revised method named ant clustering algorithm with obstacle constraints(ACAOC) based on the basic ant model. This algorithm avoids some defects of other spatial...
Clustering is one of the most important analysis tasks in spatial databases. However, in many real applications, it is more meaningful constrained clustering objects on a spatial network (e.g. road network including traffic information). The existing methods don't refer to the constrained condition. It is therefore difficult to apply them to a real road network. This paper proposes the model of clustering...
With the development of WEBGIS, GML is becoming a common way of storing spatial data. GML is an application of XML in geographic information system. In this paper, a novel algorithm SCAR-GML is proposed for spatial clustering in GML data. Compared with other spatial clustering algorithms, SCAR-GML clusters spatial objects based on the spatial adjacent relations, while the reported algorithms like...
Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon...
The method of cluster analysis is usually adopted in spatial data mining research, and this paper studies the theory of the two popular methods of cluster analysis, investigates the interesting ratio of 108 features and attributes in spatial database, uses these two methods respectively to analyze the statistics data and compare the two analysis to get the close results, at last, classifies the current...
Clustering spatial data is a well-known problem that has been extensively studied. Although many methods have been proposed in the literature, but few have handled the spatial constraints properly, which may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount...
The task of outlier identification is to find small groups of data objects that are exceptional when compared with rest large amount of data. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card frauds, voting irregularity analysis, data cleansing, network intrusion, severe weather prediction & many more. This...
The scale of spatial data is usually very large. Clustering algorithm needs very high performance, good scalability, and able to deal with noise data and high-dimensional data. Proposed a quickly clustering algorithm based on one-dimensional distance calculation. The algorithm first partitions space-sets by one-dimensional distance, then clusters space-sets by set-distance and set-density. Next, uses...
Seismic exploration plays an important role in petroleum industry. It is widely admitted that there are a lot of limitations of conventional data analysis ways in oil and gas industry. Traditional methods in petroleum engineering are knowledge-driven and often neglect some underlying factors. On the contrary, data mining is to deal with mass of data and never overlook any important phenomena. Due...
Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM).In this paper, we propose an advanced Particle swarm optimization (PSO) and differential evolution (DE) method for SCOC. In the process of doing so,we first developed a novel spatial obstructed distance using PSO-DV(particle swarm optimization with differentially perturbed Velocity) based on grid...
Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM). In this paper, we propose an improved ant colony optimization (IACO) and hybrid particle swarm optimization (HPSO) method for SCOC. In the process of doing so, we first use IACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop...
In the real world applications, application severs often receive a lot of KNN requests. To achieve better processing performance, the efficient processing of multiple KNN queries becomes a challenging research issue. This paper studies the multiple KNN queries processing techniques in constrained spatial networks. We propose an efficient cluster-bound-refine algorithm that clusters both the multiple...
Spatial clustering has been an active research area in the data mining community. Spatial clustering is not only an important effective method but also a prelude of other task for spatial data mining (SDM).In this paper, we propose a novel spatial clustering with obstacles constraints (SCOC) using an advanced hybrid particle swarm optimization (HPSO) with GA mutation based on grid model. In the process...
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