Outlier Detection in Spatial Databases Using Clustering Data Mining

Karmaker, Amitava; Rahman, Syed Mahbubur

doi:10.1109/itng.2009.198

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The ability to detect outliers can be improved using a combined perspective of outlier detection and clustering. Some clustering algorithms [7][8][9][10][11][12][13][14][15][16] handle outliers as special observations, but their main concern is clustering the dataset, not detecting outliers. The following techniques have been proposed to detect outliers: Begin 1.…”

Section: Outlier Detection Methodsmentioning

confidence: 99%

Outlier Detection using Improved Genetic K-means

Marghny¹,

Taloba²

2011

IJCA

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The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering.In this article, we present an algorithm that provides outlier detection and data clustering simultaneously. The algorithmimprovesthe estimation of centroids of the generative distribution during the process of clustering and outlier discovery. The proposed algorithm consists of two stages. The first stage consists of improved genetic k-means algorithm (IGK) process, while the second stage iteratively removes the vectors which are far from their cluster centroids.

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Section: Outlier Detection Methodsmentioning

confidence: 99%

Outlier Detection using Improved Genetic K-means

Marghny¹,

Taloba²

2011

IJCA

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“…More than a decade, we in the process of implementation of precision agriculture in the national "863" project, due to the use of computers, networks, 3S technology has accumulated a large number of spatial data, these spatial data space, timeliness, multi-dimensional, the characteristics of the mass, complexity and uncertainty [5]. In order to efficiently use existing resources in agriculture to achieve the production target optimization run, you must find out the land within the soil properties and productivity of spatial variability of circumstances, and to conduct a comprehensive analysis of these results and the correct evaluation of [6,7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Multi-Scale Fuzzy Clustering Algorithm Application in the Evaluation of Soil Fertility

Cao

et al. 2013

AMR

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This paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment of clustering model algorithm design, code realization and finally a lot of different data set of test, choose soil data as a test database, will be in the database on a large number of data mining experiment to verify the performance of the proposed algorithm. The test result feedback back will further deepen the theoretical research or correct theory already mistakes, new theory and will continue to guide experiments, both mutual promoting common development.

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“…In order to efficiently use existing resources in agriculture to achieve the production target optimization run, you must find out the land within the soil properties and productivity of spatial variability of circumstances, and to conduct a comprehensive analysis of these results and the correct evaluation of [6,7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised multi-scale fuzzy clustering algorithm application in the evaluation of soil fertility

Cao¹,

Yu²,

Ma³

et al. 2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-this paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment of clustering model algorithm design, code realization and finally a lot of different data set of test, choose soil data as a test database, will be in the database on a large number of data mining experiment to verify the performance of the proposed algorithm. The test result feedback back will further deepen the theoretical research or correct theory already mistakes, new theory and will continue to guide experiments, both mutual promoting common development.

show abstract

Outlier Detection in Spatial Databases Using Clustering Data Mining

Cited by 6 publications

References 3 publications

Outlier Detection using Improved Genetic K-means

Outlier Detection using Improved Genetic K-means

Unsupervised Multi-Scale Fuzzy Clustering Algorithm Application in the Evaluation of Soil Fertility

Unsupervised multi-scale fuzzy clustering algorithm application in the evaluation of soil fertility

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