The use of simulated annealing for clustering data in databases

McErlean, F. J.; Bell, David A.; McClean, Sally

doi:10.1016/0306-4379(90)90037-p

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…A number of different clustering approaches have been used to perform LU/LC classifications using remotely sensed data, but were found to suffer from the local minimum problem (Hegarat-Mascle et al, 1996;Klein and Dubes, 1989;Kolonko and Tran, 1997). SA has long been successfully used in various clustering applications (Brown and Huntley, 1992;Khorram et al, 2011;McErlean et al, 1990;Selim and Alsultan, 1991), making it worthwhile research to develop and investigate SA-based classification systems for LU/LC classification using remotely sensed data due to being able to overcome the local minimum problem (Dai and Khorram, 1999;Geman and Geman, 1984;Pang, Chen and Chen, 2006;Yuan, Van Der Wiele, and Khorram, 2009). …”

Section: Simulated Annealing: Basic Principlesmentioning

confidence: 99%

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Wiele

Khorram

Yuan

2012

ESR

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Local minima limitations in unsupervised approaches using K-means is still problematic in producing accurate land use/land cover classifications. In response, we developed algorithms of Simulated Annealing (SA) systems based on K-means. We hypothesized that SA-based systems can reduce the likelihood of converging on a local minimum. Two automated SA-based classification systems were developed and applied to a Landsat TM data: a single SA-based (S-SA) system and an integrated SA-based (I-SA) system, which reduces computational intensity. We hypothesized that the I-SA system could produce more efficient classifications than the S-SA system. Kappa statistical analysis on the resulting error matrices demonstrated that the SA-based system significantly improved the classification accuracy over that of the K-means algorithm when appropriate parameters combined as a cooling schedule were chosen. The knowledge and insights gained can facilitate the incorporation of SA random search procedures into other approaches that are similarly limited by local minimum problems to improve accuracy.

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Section: Simulated Annealing: Basic Principlesmentioning

confidence: 99%

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Wiele

Khorram

Yuan

2012

ESR

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“…Although it is recognized as being an exceedingly difficult problem, object clustering has recently become a popular topic [1]. The problem has clear links with clustering in relational database systems [2,3] and it is closely related to clustering in Information Retrieval (e.g. [4]), and in Artificial Intelligence (e.g.…”

Section: Introductionmentioning

confidence: 99%

Experimental Studies of Heuristic Algorithms for Two Level Object Clustering

Bell

McErlean

1994

Kybernetes

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The clustering of objects in a layered object storage system is by common consent an exceedingly difficult problem. Studies the performance of three heuristic placement algorithms. A series of eight reasonably realistic case studies were used as a benchmark battery, and several hundred experiments were carried out to evaluate results of using the algorithms. Presents the results and the insights gained from the study.

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“…Hard clustering divides data into disjoint clusters while soft clustering allows data elements to belong to more than one cluster. Existing techniques include MCL [41], Ncut [40], graclus [13], MCODE [5], iterative scan [9], k-clique-community [37], spectral clustering [35,32], simulated annealing [28], or partitioning using network flow [34], edge centrality [18], and many other techniques e.g. [42].…”

Section: Prior Work On Measuresmentioning

confidence: 99%

Spatial Scan Statistics for Graph Clustering

Wang

Phillips

Schreiber

et al. 2008

Proceedings of the 2008 SIAM International Conference on Data Mining

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In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected edges in a subgraph. This measure is adapted from spatial scan statistics for point sets and provides quantitative assessment for clusters. We discuss some important properties of this statistic and its relation to modularity and Bregman divergences. We apply a simple clustering algorithm to find clusters with large values of this measure in a variety of real-world data sets, and we illustrate its ability to identify statistically significant clusters of selected granularity.

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The use of simulated annealing for clustering data in databases

Cited by 11 publications

References 14 publications

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Experimental Studies of Heuristic Algorithms for Two Level Object Clustering

Spatial Scan Statistics for Graph Clustering

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