Data mining for selective visualization of large spatial datasets

Sekhar, S. R. Mani; Lu, Chang-Tien; Zhang, Pusheng; Liu, Rulin

doi:10.1109/tai.2002.1180786

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…Second, while classical data mining works with explicit inputs, spatial predicates, and attributes are often implicit. Third, classical data mining treats each input independently of other inputs, while spatial patterns often exhibit continuity and high autocorrelation among nearby features (Shekhar et al, 2002). Fayyad et al (1996) used decision tree methods to classify images of stellar objects to detect stars and galaxies.…”

Section: Mining African Massive Data 41 Mining Spatial Databasesmentioning

confidence: 99%

Generating metadata to study and teach about African issues

Alshameri

Bangura

2014

Information Technology &Amp; People

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Purpose -After almost three centuries of employing western educational approaches, many African societies are still characterized by low western literacy rates, civil conflicts, and underdevelopment. It is obvious that these western educational paradigms, which are not indigenous to Africans, have done relatively little good for Africans. Thus, the purpose of this paper is to argue that the salvation for Africans hinges upon employing indigenous African educational paradigms which can be subsumed under the rubric of ubuntugogy, which the authors define as the art and science of teaching and learning undergirded by humanity toward others. Design/methodology/approach -Therefore, ubuntugogy transcends pedagogy (the art and science of teaching), andragogy (the art and science of helping adults learn), ergonagy (the art and science of helping people learn to work), and heutagogy (the study of self-determined learning). That many great African minds, realizing the debilitating effects of the western educational systems that have been forced upon Africans, have called for different approaches. Findings -One of the biggest challenges for studying and teaching about Africa in Africa at the higher education level, however, is the paucity of published material. Automated generation of metadata is one way of mining massive data sets to compensate for this shortcoming. Originality/value -Thus, the authors address the following major research question in this paper: What is automated generation of metadata and how can the technique be employed from an African-centered perspective? After addressing this question, conclusions and recommendations are offered.

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Section: Mining African Massive Data 41 Mining Spatial Databasesmentioning

confidence: 99%

Generating metadata to study and teach about African issues

Alshameri

Bangura

2014

Information Technology &Amp; People

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“…The CubeView system [6] facilitates the observation of spatial patterns and temporal trends in large volumes of data. While the focus lies on spatial data warehouses with efficient spatial mining and analysis processes, multiple visualizations in different dimensions play an important role in order to interpret the results.…”

Section: Related Workmentioning

confidence: 99%

Interactive visual exploration of multidimensional data: requirements for CommonGIS with OLAP

Voß

Hernández

Voss

et al. 2004

Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004.

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“…Data mining is a process to extract implicit, nontrivial, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases [1,2]. The explosive growth in data and databases used in business managements, government administration, and scientific data analysis has created a need for tools that can automatically transform the processed data into useful information and knowledge [3]. Spatial data mining as a subfield of data mining refers to the extraction from spatial databases of implicit knowledge, spatial relations or significant features or patterns that are not explicitly stored in spatial databases [4].…”

Section: Introductionmentioning

confidence: 99%

“…Spatial datasets and patterns are abundant in many application domains related to the Environmental Protection Agency, the National Institute of standards and Technology, and the Department of Transportation. Challenges in spatial data mining arise from the following issues [3,5]. Firstly, classical data mining is designed to process numbers and categories.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation

Yang¹,

Cui²

2010

JSEA

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Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers

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Data mining for selective visualization of large spatial datasets

Cited by 14 publications

References 19 publications

Generating metadata to study and teach about African issues

Generating metadata to study and teach about African issues

Interactive visual exploration of multidimensional data: requirements for CommonGIS with OLAP

A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation

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