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A Shrinking-Based Approach for Multi-Dimensional Data Analysis [chapter]

Yong Shi, Yuqing Song, Aidong Zhang
2003 Proceedings 2003 VLDB Conference  
Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multi-dimensional data analysis which consists of three steps: data shrinking, cluster detection  ...  Following data shrinking, clusters are detected by finding the connected components of dense cells.  ...  Application of shrinking preprocessing to multi-dimensional data analysis To demonstrate the advantages of the data shrinking preprocessing, we applied it to the multi-dimensional clustering problem which  ... 
doi:10.1016/b978-012722442-8/50046-x dblp:conf/vldb/ShiSZ03 fatcat:vfgmryl7bzbk3gvzl3652pyl7m


Yong Shi
2008 Proceedings of the 46th Annual Southeast Regional Conference on XX - ACM-SE 46  
Shrinking [19] is a novel data preprocessing technique which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitation [16] in the real world.  ...  In this paper, we applied the Fuzzy concept to improve the performance of the shrinking approach, targeting the better decision making for the movement for individual data points in each iteration.  ...  In the previous work [19] , we proposed a shrinking-based approach for multi-dimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection  ... 
doi:10.1145/1593105.1593174 dblp:conf/ACMse/Shi08a fatcat:b3nrwrisabc7xa4b4lq5xgxuly

Efficient Support for Similarity Searches in DHT-Based Peer-to-Peer Systems

J. Gao, P. Steenkiste
2007 2007 IEEE International Conference on Communications  
We propose tree compressing and node shrinking techniques to efficiently support applications with high dimensionality datasets.  ...  Simulation results using both synthetic and real data show the effectiveness of our system.  ...  ACKNOWLEDGMENT This research was funded in part by NSF under award number CCR-0205266 and was performed while the first author was a graduate student at Carnegie Mellon University.  ... 
doi:10.1109/icc.2007.311 dblp:conf/icc/GaoS07 fatcat:y5ne6bhgdfbpzp7e43qh6wqu5u

Dynamical masses, time-scales, and evolution of star clusters [article]

Ortwin Gerhard
2000 arXiv   pre-print
This review discusses (i) dynamical methods for determining the masses of Galactic and extragalactic star clusters, (ii) dynamical processes and their time-scales for the evolution of clusters, including  ...  These processes lead to significant evolution of globular cluster systems after their formation.  ...  It then goes on to describe a number of dynamical processes and their time-scales which will lead to evolution and potentially destruction of star clusters over long time-scales. 1 2 Finally, the results  ... 
arXiv:astro-ph/0007258v1 fatcat:gpg4oyj3ajdglk6brbhjaobuie

A statistical test for Nested Sampling algorithms

Johannes Buchner
2014 Statistics and computing  
Nested sampling is an iterative integration procedure that shrinks the prior volume towards higher likelihoods by removing a "live" point at a time.  ...  If the drawn points are distributed uniformly, the removal of a point shrinks the volume in a well-understood way, and the integration of nested sampling is unbiased.  ...  I acknowledge funding through a doctoral stipend by the Max Planck Society. This manuscript has greatly benefited from the comments of the two anonymous referees, whom I would also like to thank.  ... 
doi:10.1007/s11222-014-9512-y fatcat:5xvoyhvthnhafgn22rxklcjw2i

Data Clustering and Visualization Using Cellular Automata Ants [chapter]

Andrew Vande Moere, Justin J. Clayden, Andy Dong
2006 Lecture Notes in Computer Science  
Cellular ants demonstrates how a decentralized multi-agent system can autonomously detect data similarity patterns in multi-dimensional datasets and then determine the according visual cues, such as position  ...  This method merges characteristics of ant-based data clustering and cellular automata to represent complex datasets in meaningful visual clusters.  ...  Multi-dimensional scaling (MDS) displays the structure of distance-like datasets as geometrical pictures [7] .  ... 
doi:10.1007/11941439_87 fatcat:iss6jok5mfhihasxgdmeyobzli

A coupled autoencoder approach for multi-modal analysis of cell types [article]

Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül
2019 arXiv   pre-print
only by a single modality.  ...  The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types.  ...  (i) Representations shrink to zero in the absence of scaling (Eq.2).  ... 
arXiv:1911.05663v1 fatcat:kbjhjmo4qjatzmw5phk3mgkaaa

Volume exploration using ellipsoidal Gaussian transfer functions

Yunhai Wang, Wei Chen, Guihua Shan, Tingxin Dong, Xuebin Chi
2010 2010 IEEE Pacific Visualization Symposium (PacificVis)  
Our approach explores volumetric features in the statistical space by modeling the space using the Gaussian mixture model (GMM) with a small number of Gaussians to maximize the likelihood of feature separation  ...  Instant visual feedback is possible by mapping these Gaussians to ETFs and analytically integrating these ETFs in the context of the pre-integrated volume rendering process.  ...  This paper is partially supported by Knowledge Innovation Project of The Chinese Academy of Sciences  ... 
doi:10.1109/pacificvis.2010.5429612 dblp:conf/apvis/WangCSDC10 fatcat:ia6ygoxnsjgs7h2nm3asnihjnu

Nested Sampling Methods [article]

Johannes Buchner
2021 arXiv   pre-print
The relation between number of live points, dimensionality and computational cost is studied for two complete algorithms.  ...  Its strengths are the unsupervised navigation of complex, potentially multi-modal posteriors until a well-defined termination point.  ...  These reduce the number of likelihood evaluations by excluding space that is likely outside the contour, while retaining the dimensionality scaling of MCMC algorithms.  ... 
arXiv:2101.09675v2 fatcat:znsb2uhay5d3ja4qc37znrgmuu

Grain Growth in Large-Scale Molecular Dynamics Simulation: Linkage between Atomic Configuration and von Neumann-Mullins Relation

Shin Okita, Yasushi Shibuta
2016 ISIJ International  
Part of this work was supported by the Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) and the High Performance Computing Infrastructure (HPCI) in Japan  ...  Acknowledgement This work was supported by Grant-in-Aid for Scientific Research (B) (No. 16H04490) from Japan Society for the Promotion of Science, and the 23rd ISIJ Research Promotion Grants from the  ...  In the case of the three-dimensional grain growth, the threshold number of neighboring grains dividing growth and shrink is approximately 14.  ... 
doi:10.2355/isijinternational.isijint-2016-408 fatcat:7izbxq3tvzf6zajxffttepl3qq

Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space

W. Dzwinel, D. A. Yuen, K. Boryczko, Y. Ben-Zion, S. Yoshioka, T. Ito
2005 Nonlinear Processes in Geophysics  
Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters  ...  We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs.  ...  into 3-D space by using multi-dimensional scaling (MDS).  ... 
doi:10.5194/npg-12-117-2005 fatcat:j6xde67k2zdebho3lyhoe3wik4

On cluster tree for nested and multi-density data clustering

Xutao Li, Yunming Ye, Mark Junjie Li, Michael K. Ng
2010 Pattern Recognition  
The NBC is an extension to DBSCAN based on a neighborhood based density factor, which makes it capable of dealing with multi density clusters.  ...  The LDBSCAN extends the local density factor [11] to encode the local density information, which makes it capable of detecting multi density clusters.  ...  We set the number of clusters as the genuine number of it. We set the shrink factor to be 0.5 which was in the range of 0.2-0.7 suggested by the author s [19] .  ... 
doi:10.1016/j.patcog.2010.03.020 fatcat:enwirwipsfg43jdytjhcxkxjfe

Clustering very large multi-dimensional datasets with MapReduce

Robson Leonardo Ferreira Cordeiro, Caetano Traina, Agma Juci Machado Traina, Julio López, U. Kang, Christos Faloutsos
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Spanning 0.2 TB of multi-dimensional data, it took only 8 minutes to be clustered, using 128 cores.  ...  our reasonable defaults), it matches the clustering quality of the serial algorithm, and it has near-linear scale-up; and finally, (3) We report experiments on real and synthetic data with billions of  ...  Although spanning 0.2 TB of multi-dimensional data, BoW took only 8 minutes to cluster it, using 128 cores.  ... 
doi:10.1145/2020408.2020516 dblp:conf/kdd/CordeiroTTLKF11 fatcat:s5xi6ty2efeudojr5b2q6p3wcy

Research on Rapid Assembly Technology of Helicopter Transmissions

Wang Haiwei, Li Lulu, Yuan Sheng, M.H.A. Shukor
2020 MATEC Web of Conferences  
An assembly model of helicopter transmissions is presented by introducing Geometric Constraint Graph (GCG) method. A hierarchical model is obtained by multi-shrink decomposition.  ...  Research and application of rapid assembly technology can widely improve the design efficiency of helicopter transmissions.  ...  Acknowledgements This research is supported by the 111 project (no. B13044) and the concept research of new rotorcraft project (no.30103090101).  ... 
doi:10.1051/matecconf/202030605004 fatcat:k7u3vwlhwjelrgznsidpeyapda

Two-way analysis of high-dimensional collinear data

Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski
2009 Data mining and knowledge discovery  
We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional  ...  Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way  ...  The optimal number of clusters is chosen by predictive likelihood, recovering the correct number of clusters K = 4 (Fig. 2) .  ... 
doi:10.1007/s10618-009-0142-5 fatcat:d62mndvfifbfhkjzfhf2zjbjca
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