• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.119-132
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• 2009

Owing to the GIS technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. In this paper, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of three parts: a graphical user interface for inputs and outputs, a data mining module that processes spatial mining functionalities, a data storage model that stores and manages spatial as well as non-spatial data by using a DBMS. In particular, the data mining module provides major data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. SD-Miner has own characteristics: (1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively; (2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions. (3) It inputs parameters for mining as a form of database tables to increase flexibility. In order to verify the practicality of our SD-Miner developed, we present meaningful results obtained by performing spatial data mining with real-world spatial data.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.89-97
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• 2019

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

• Spatial Information Research
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• v.19 no.2
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• pp.65-70
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• 2011

Advertizing postal system combined with GIS and temporal/spatial mining techniques has been developed to activate advertizing service and conduct marketing campaign efficiently. In order to select customers accurately, this system provide purchase propensity information using sequential, cyclicpatterns and lifesytle information through RFM analysis and clustering technique. It is possible for corporate mailer to do customer oriented marketing campaign with the advertizing postal system as well as 'one-stop' service including target customer selection, mail production, and delivery request.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.613-624
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• 2022

A study of the evolution of overburden fractures under the solid-fluid coupling state was conducted based on the geological and mining characteristics of the coal seam depth, weak strata cementation, and high-intensity mining in the mining areas of West China. These mining characteristics are key to achieving water conservation during mining or establishing groundwater reservoirs in coal mines. Based on the engineering background of the Daliuta Coal Mine, a non-hydrophilic simulation material suitable for simulating the weakly cemented rock masses in this area was developed, and a physical simulation test was carried out using a water-sand gushing test system. The study explored the spatial distribution and dynamic evolution of the fractured zone in the mining overburden under the coupling of stress and seepage. The experimental results show that the mining overburden can be vertically divided into the overall migration zone, the fracture extension zone and the collapse zone; additionally, in the horizontal direction, the mining overburden can be divided into the primary fracture zone, periodic fracture zone, and stop-fracture zone. The scope of groundwater flow in the overburden gradually expands with the mining of coal seams. When a stable water inrush channel is formed, other areas no longer generate new channels, and the unstable water inrush channels gradually close. Finally, the primary fracture area becomes the main water inrush channel for coal mines. The numerical simulation results indicate that the overlying rock breaking above the middle of the mined-out area allows the formation of the water-conducting channel. The water body will flow into the fracture extension zone with the shortest path, resulting in the occurrence of water bursting accidents in the mining face. The experimental research results provide a theoretical basis for the implementation of water conservation mining or the establishment of groundwater reservoirs in western mining areas, and this theoretical basis has considerable application and promotion value.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2786-2796
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• 1998

Extraction of interesting and general knowledge from large spatial database is an important task in the development of geographical information system and knowledge-base systems. In this paper, we propose a spatial data mining system using generalization method; In this system, we extend an existing generalization mining and design a rulebase to support deriving new spatial knowledge. For this purpose, we propose an interleaved method which integrates spatial data dominated and nonspatial data dominated mining and construct a rulebase to extract topological relationship between spatial objects.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.49-56
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• 2010

Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.121-128
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• 2010

Spatial data mining has been developed to support spatial association knowledge between spatial features or its non-spatial attributes for an application areas. At the present time, a number of researchers attempt to the data mining techniques apply to the several analysis areas, for examples, civil engineering, environmental, agricultural areas. Despite the efforts that, until such time as not existed practical systems for the gCRMDMs. gCRMDMs is merged with very large spatial database and CRM information system. Also, it is discovery the association rule for the predictions of customer's shopping pattern informations in a huge database consisted with spatial and non-spatial dataset. For this goal, gCRMDMs need spatial data mining techniques. But, nowadays, in a most case not exist utilizable model for the gCRMDMs. Therefore, in this paper, we proposed a practical gCRMDMs model to support a customer, store, street, building and geographical suited to the trade area.

• Journal of the Korean Geographical Society
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• v.39 no.3
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• pp.457-472
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• 2004

The purpose of this study is to analyze spatial patterns of traffic accidents and to investigate spatial relations among neighboring spatial objects by applying GIS and spatial data mining methods. This study investigated traffic accident data in Kangnam-gu, Seoul, as a case study. As a result, four clusters were emerged based on individual attributes of traffic accidents. Each cluster showed distinctive properties. In spatial associations between individual attributes of traffic accidents and neighboring spatial objects, there were many rules according to concept hierarchy and definition of spatial relations. Although all rules were not be interesting and significant, they could be a clue to investigate more.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.567-576
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• 2003

Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exists in spatial databases, is a challenging task due to the huge amounts of spatial data. Clustering algorithms are attractive for the task of class identification in spatial databases. Several methods for spatial clustering have been presented in recent years, but have the following several drawbacks increase costs due to computing distance among objects and process only memory-resident data. In this paper, we propose an efficient grid cell based spatial clustering method for spatial data mining. It focuses on resolving disadvantages of existing clustering algorithms. In details, it aims to reduce cost further for good efficiency on large databases. To do this, we devise a spatial clustering algorithm based on grid ceil structures including cell relationships.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.599-617
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• 2006

Recently many LBS(Location Based Service) systems are issued in mobile computing systems. Spatial-Temporal Moving Sequence Pattern Mining is a new mining method that mines user moving patterns from user moving path histories in a sensor network environment. The frequent pattern mining is related to the items which customers buy. But on the other hand, our mining method concerns users' moving sequence paths. In this paper, we consider the sequence of moving paths so we handle the repetition of moving paths. Also, we consider the duration that user spends on the location. We proposed new Apriori_msp based on the Apriori algorithm and evaluated its performance results.