The BANG-clustering system: Grid-based data analysis

Schikuta, Erich; Erhart, Martin

doi:10.1007/bfb0052867

Cited by 52 publications

(21 citation statements)

References 11 publications

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“…For example, quadrat counts have been employed to study plant distributions (Gleason 1920;Skellam 1952;Clark and Evans 1954), land use patterns (Getis 1964), and the distribution of towns (Dacey 1964) to determine whether or not a point pattern is clustered. In recent years, this approach has been made more exploratory to find clusters on a grid of small quadrats, known as grid-based cluster detection (Morphet 1997;Schikuta and Erhart 1997;Wang, Yang, and Muntz 1997). Another traditional approach is the use of nearest-neighbor statistics (Clark and Evans 1954), and this has since been extended to use any number of nearest neighbors (Thompson 1956) and to detect clusters in both space and time (Ederer, Myers, and Mantel 1964;Knox and Bartlett 1964;David and Barton 1966).…”

Section: What Has Been Done Before?mentioning

confidence: 99%

A Genetic Approach to Detecting Clusters in Point Data Sets

Conley

Gahegan

Macgill

2005

Geographical Analysis

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Spatial analysis techniques are widely used throughout geography. However, as the size of geographic data sets increases exponentially, limitations to the traditional methods of spatial analysis become apparent. To overcome some of these limitations, many algorithms for exploratory spatial analysis have been developed. This article presents both a new cluster detection method based on a genetic algorithm, and Programs for Cluster Detection, a toolkit application containing the new method as well as implementations of three established methods: Openshaw's Geographical Analysis Machine (GAM), case point-centered searching (proposed by Besag and Newell), and randomized GAM (proposed by Fotheringham and Zhan). We compare the effectiveness of cluster detection and the runtime performance of these four methods and Kulldorf's spatial scan statistic on a synthetic point data set simulating incidence of a rare disease among a spatially variable background population. The proposed method has faster average running times than the other methods and significantly reduces overreporting of the underlying clusters, thus reducing the user's postprocessing burden. Therefore, the proposed method improves upon previous methods for automated cluster detection. The results of our method are also compared with those of Map Explorer (MAPEX), a previous attempt to develop a genetic algorithm for cluster detection. The results of these comparisons indicate that our method overcomes many of the problems faced by MAPEX, thus, we believe, establishing that genetic algorithms can indeed offer a viable approach to cluster detection.

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Section: What Has Been Done Before?mentioning

confidence: 99%

A Genetic Approach to Detecting Clusters in Point Data Sets

Conley

Gahegan

Macgill

2005

Geographical Analysis

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“…BangA performs a density-based clustering on data buckets in a way similar to BANGclustering [33]. BANG-clustering is a grid clustering approach that relies on a main memory kd-tree accommodated from the BANG file.…”

Section: Density-based Clusteringmentioning

confidence: 99%

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Anjum

Raschia

2017

Computers

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Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions.

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“…The second level then refines first level clustering using the user-supplied fixed radius and tries to detect subclusters at every first level cluster, and so forth. Initial neuron settings at each hierarchical level are generated due to probability densities at fixed cells in vector space using a cell-like clustering similar to the BANG-clustering system by Schikuta and Erhar [24].…”

Section: Neural Network Clusterisation Hierarchical Radius-based Compmentioning

confidence: 99%

Application of Artificial Neural Networks for Different Engineering Problems

Bogdan¹

1999

SOFSEM’99: Theory and Practice of Informatics

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This paper presents some applications of data and signal processing using artificial neural nets (ANNs) which have been investigated at the University of Tübingen. The applications covering a wide range of different interesting domains: color restoration, gas sensing systems, internet information search and delivery, online quality control and nerve signal processing. In order to achieve the requirements of each application supervised learning as well as unsupervised learning artificial neural nets have been used. The paper presents each application in detail and describes the problems which have been solved using artificial neural nets.

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The BANG-clustering system: Grid-based data analysis

Cited by 52 publications

References 11 publications

A Genetic Approach to Detecting Clusters in Point Data Sets

A Genetic Approach to Detecting Clusters in Point Data Sets

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Application of Artificial Neural Networks for Different Engineering Problems

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