The Experts below are selected from a list of 12354 Experts worldwide ranked by ideXlab platform
Key-ho Park - One of the best experts on this subject based on the ideXlab platform.
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Discovery Science - Classification of changing regions based on temporal context in local Spatial Association
Discovery Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method of modeling regional changes in local Spatial Association and classifying the changing regions based on the similarity of time-series signature of local Spatial Association. For intuitive recognition of time-series local Spatial Association, we employ Moran scatterplot and extend it to QS-TiMoS (Quadrant Sequence on Time-series Moran Scatterplot) that allows for examining temporal context in local Spatial Association using a series of categorical variables. Based on the QS-TiMoS signature of nodes and edges, we develop the similarity measures for “state sequence” and “clustering transition” of time-series local Spatial Association. The similarity matrices generated from the similarity measures are then used for producing the classification maps of time-series local Spatial Association that present the history of changing regions in clusters. The feasibility of the proposed method is tested by a case study on the rate of land price fluctuation of 232 administrative units in Korea, 1995-2004.
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Web-based cluster analysis for the time-series signature of local Spatial Association
Lecture Notes in Computer Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of state sequence and clustering transition for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
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W2GIS - Web-based cluster analysis for the time-series signature of local Spatial Association
Web and Wireless Geographical Information Systems, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of “state sequence” and “clustering transition” for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
Tao Chen - One of the best experts on this subject based on the ideXlab platform.
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Efficient discovery of multilevel Spatial Association rules using partitions
Information and Software Technology, 2005Co-Authors: Lizhen Wang, Kunqing Xie, Tao ChenAbstract:Spatial data mining has been identified as an important task for understanding and use of Spatial data- and knowledge-bases. In this paper, we present a new approach to discover strong multilevel Spatial Association rules in Spatial databases based on partitioning the set of rows with respect to the Spatial relations denoted as relation table R. Meanwhile, the introduction of the equivalence partition tree makes the discovery of multilevel Spatial Association rules easy and efficient. Experiments show that the new algorithm is efficient.
Jae-seong Ahn - One of the best experts on this subject based on the ideXlab platform.
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Classification of Changing Regions Using a Temporal Signature of Local Spatial Association
Environment and Planning B: Planning and Design, 2009Co-Authors: Jae-seong Ahn, Hwahwan Kim, Yang-won LeeAbstract:Spatially associated patterns are often found in geographical phenomena, since nearby entities are often more related than distant ones. Such Spatial Association also changes over time; hence, the temporal aspect of Spatial Association needs to be examined using both Spatiality and temporality. This paper describes a method of modeling the temporal signatures of Spatial Association, and thus of grouping similar changes. We employed a Moran scatterplot to assess the local characteristics of a Spatial Association and then extended it to a time-series Moran scatterplot quadrant signature (MSQS) to capture spatiotemporal changes in regions categorically. We used sequence comparison and data grouping techniques to classify similar regions in terms of the time-series MSQS. We tested the feasibility of the proposed method using a case study of a twenty-four-month (June 2004–May 2006) housing price index for sixty-nine administrative units in the Seoul Metropolitan Area, South Korea.
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Discovery Science - Classification of changing regions based on temporal context in local Spatial Association
Discovery Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method of modeling regional changes in local Spatial Association and classifying the changing regions based on the similarity of time-series signature of local Spatial Association. For intuitive recognition of time-series local Spatial Association, we employ Moran scatterplot and extend it to QS-TiMoS (Quadrant Sequence on Time-series Moran Scatterplot) that allows for examining temporal context in local Spatial Association using a series of categorical variables. Based on the QS-TiMoS signature of nodes and edges, we develop the similarity measures for “state sequence” and “clustering transition” of time-series local Spatial Association. The similarity matrices generated from the similarity measures are then used for producing the classification maps of time-series local Spatial Association that present the history of changing regions in clusters. The feasibility of the proposed method is tested by a case study on the rate of land price fluctuation of 232 administrative units in Korea, 1995-2004.
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Web-based cluster analysis for the time-series signature of local Spatial Association
Lecture Notes in Computer Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of state sequence and clustering transition for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
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W2GIS - Web-based cluster analysis for the time-series signature of local Spatial Association
Web and Wireless Geographical Information Systems, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of “state sequence” and “clustering transition” for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
Raghvendra Kumar - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Spatial Association Rule Mining
International Journal of Research, 2016Co-Authors: Ranu Sahu, Raghvendra KumarAbstract:The Association rule mining technique emerged with the objective to find novel, useful, and previously unknown Associations from transactional databases, and a large amount of Association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and Association rules. In geographic databases the problem of mining Spatial Association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain Associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences Keywords: Data Mining; Distributed Data Mining; Association Rule Mining; Spatial Data Mining; Spatial Association Rules; Weka Tool.
Yang-won Lee - One of the best experts on this subject based on the ideXlab platform.
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Classification of Changing Regions Using a Temporal Signature of Local Spatial Association
Environment and Planning B: Planning and Design, 2009Co-Authors: Jae-seong Ahn, Hwahwan Kim, Yang-won LeeAbstract:Spatially associated patterns are often found in geographical phenomena, since nearby entities are often more related than distant ones. Such Spatial Association also changes over time; hence, the temporal aspect of Spatial Association needs to be examined using both Spatiality and temporality. This paper describes a method of modeling the temporal signatures of Spatial Association, and thus of grouping similar changes. We employed a Moran scatterplot to assess the local characteristics of a Spatial Association and then extended it to a time-series Moran scatterplot quadrant signature (MSQS) to capture spatiotemporal changes in regions categorically. We used sequence comparison and data grouping techniques to classify similar regions in terms of the time-series MSQS. We tested the feasibility of the proposed method using a case study of a twenty-four-month (June 2004–May 2006) housing price index for sixty-nine administrative units in the Seoul Metropolitan Area, South Korea.
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Discovery Science - Classification of changing regions based on temporal context in local Spatial Association
Discovery Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method of modeling regional changes in local Spatial Association and classifying the changing regions based on the similarity of time-series signature of local Spatial Association. For intuitive recognition of time-series local Spatial Association, we employ Moran scatterplot and extend it to QS-TiMoS (Quadrant Sequence on Time-series Moran Scatterplot) that allows for examining temporal context in local Spatial Association using a series of categorical variables. Based on the QS-TiMoS signature of nodes and edges, we develop the similarity measures for “state sequence” and “clustering transition” of time-series local Spatial Association. The similarity matrices generated from the similarity measures are then used for producing the classification maps of time-series local Spatial Association that present the history of changing regions in clusters. The feasibility of the proposed method is tested by a case study on the rate of land price fluctuation of 232 administrative units in Korea, 1995-2004.
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Web-based cluster analysis for the time-series signature of local Spatial Association
Lecture Notes in Computer Science, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of state sequence and clustering transition for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
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W2GIS - Web-based cluster analysis for the time-series signature of local Spatial Association
Web and Wireless Geographical Information Systems, 2006Co-Authors: Jae-seong Ahn, Yang-won Lee, Key-ho ParkAbstract:We propose a method for modeling the time-series of local Spatial Association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of “state sequence” and “clustering transition” for the time-series of local Spatial Association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local Spatial Association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local Spatial Association are implemented in a Web-based GIS using XML Web Services.
