Dual clustering: integrating data clustering over optimization and constraint domains

Lin, Cheng-Ru; Liu, Ken-Hao; Chen, Ming-Syan

doi:10.1109/tkde.2005.75

Cited by 48 publications

(8 citation statements)

References 17 publications

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“…Two experiments are conducted. The first one uses artificial data as in Reference [15], and the second uses real data of forest cover type.…”

Section: Resultsmentioning

confidence: 99%

“…Although the ICC algorithm [15] solves the dual clustering problem with an interlaced clustering-classification, the algorithm requires data preprocessing in both domains such that the interlaced procedure could not be effectively employed, and the complexity of the classification phase is intractable because of using support vector classifiers. Additionally, ICC is a centralized algorithm that could not deal with the dual clustering problem in distributed databases.…”

Section: Related Workmentioning

confidence: 99%

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DCAD: a dual clustering algorithm for distributed spatial databases

Zhou

Guan²,

Li³

2007

Geo-spatial Information Science

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Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

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“…Two experiments are conducted. The first one uses artificial data as in Reference [15], and the second uses real data of forest cover type.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DCAD: a dual clustering algorithm for distributed spatial databases

Zhou

Guan²,

Li³

2007

Geo-spatial Information Science

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“…For comparative purposes, we also introduced two typical dual spatial strategies: modified k-means (MK-means) [49] and DBSC [50]. The K-means method calculates the spatial distance of the clustering targets, while the MK-means algorithm not only focuses on the spatial clustering of the targets but also takes into account their attribute distance.…”

Section: Comparison Of Simulation Using Traditional Dual Spatial Clus...mentioning

confidence: 99%

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Zhu,

et al. 2023

Land

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In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.

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“…We formulate the sensor allocation problem as a dual clustering of the points of interest in the spatial and non-spatial domains. We try to partition the hot spot data set into several groups, so that these groups form nonoverlapping compact regions in the spatial domain while minimizing the dissimilarity of the data points in a group on the non-spatial domain (Lin et al 2005). Then, each group will be allocated one sensor, which will monitor the hot spot points associated with that group.…”

Section: Proposed Thermal Sensor Allocation and Placement Techniquesmentioning

confidence: 99%

Research of thermal sensor allocation and placement based on dual clustering for microprocessors

Meng-tian

Liu

et al. 2013

SpringerPlus

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Dynamic thermal management techniques employ a set of on-chip thermal sensors to measure runtime thermal behavior of microprocessors so as to prevent the on-set of high temperatures. Therefore, effective analysis of thermal behavior and determination of the best allocation and placement of thermal sensors directly impact the effectiveness of the dynamic thermal management mechanisms. In this paper, we propose systematic and effective techniques for determining the fewest number of thermal sensors and the optimal locations based on dual clustering to provide a high fidelity thermal monitoring. Initially, we utilize the dual clustering algorithm to devise method that can reduce the number of sensors to a great extent while satisfying an expected accuracy. Then we identify an optimal physical location for each sensor such that the sensor’s attraction towards steep thermal gradient is maximized. Experimental results indicate the superiority of our techniques and confirm that our proposed methods are capable of creating a sensor distribution for a given microprocessor architecture using the number of thermal sensors of 2, 8, 15, 24, 35, depending on different expected hot spot temperature error accuracy of 5%, 4%, 3%, 2%, 1%, respectively.

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Dual clustering: integrating data clustering over optimization and constraint domains

Cited by 48 publications

References 17 publications

DCAD: a dual clustering algorithm for distributed spatial databases

DCAD: a dual clustering algorithm for distributed spatial databases

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Research of thermal sensor allocation and placement based on dual clustering for microprocessors

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