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Probabilistic Clustering of Time-Evolving Distance Data [article]

Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir S. Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015 arXiv   pre-print
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points.  ...  We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods.  ...  However, to the best of our knowledge, no time-evolving clustering models exist that work on distance data directly, and clustering of time-evolving distance data is still an unsolved problem.  ... 
arXiv:1504.03701v1 fatcat:cp54cg3em5erdk4asv7ldorrtm

Probabilistic clustering of time-evolving distance data

Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir S. Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015 Machine Learning  
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points.  ...  We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods.  ...  Access to patient data is covered under IRB Waiver #WA0426-13.  ... 
doi:10.1007/s10994-015-5516-x fatcat:oiisxrevb5fqfjd2w6qvr677s4

Evolutionary Soft Co-Clustering [chapter]

Wenlu Zhang, Shuiwang Ji, Rui Zhang
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
We consider the mining of hidden block structures from time-varying data using evolutionary co-clustering.  ...  An appealing feature of the proposed probabilistic model is that it leads to soft co-clustering assignments naturally.  ...  This research is supported by NSF DBI-1147134 and by Old Dominion University Office of Research.  ... 
doi:10.1137/1.9781611972832.14 dblp:conf/sdm/JiZZ13 fatcat:xxnjstf3krfippysgjwbf4nr6u

Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks

Yevgeniy Bodyanskiy, Olena Vynokurova, Ilya Kobylin, Oleg Kobylin
2016 Information Technology and Management Science  
The clustering-segmentation task of short time series with unevenly distributed observations (at the same time in all samples) is considered.  ...  In the paper, adaptive modifications of fuzzy clustering methods have been proposed for solving the problem of data stream mining in online mode.  ...  In this connection, similarity measure of time series PS-distance (Piecewise slope distance = PS -distance = STSdistance = short time series distance) has been introduced in [8] , based on representing  ... 
doi:10.1515/itms-2016-0006 fatcat:ynopo7xq5rfjdebaavnqsy5j6u

Group Pattern Mining on Moving Objects' Uncertain Trajectories

Shuang Wang, Lina Wu, Fuchai Zhou, Cuicui Zheng, Haibo Wang
2015 International Journal of Computers Communications & Control  
Unfortunately, most previous research on trajectory pattern mining did not consider the uncertainty of trajectory data.  ...  In the first step, the uncertain objects' similarities are computed according to their expected distances at each timestamp, and then the objects are clustered according to their spatial proximity.  ...  We can use the Evolving Density Estimator [8] to compute the mean value u and standard deviation v of an object's position at each time.  ... 
doi:10.15837/ijccc.2015.3.1667 fatcat:nx43jexlwngxfh5nekb2evqcfq

Analysis of Small-World Features in Vehicular Social Networks

Anna Maria Vegni, Valeria Loscri, Pietro Manzoni
2019 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)  
Moreover, the use of a probabilistic data dissemination protocol based on social features [1] proves that the time-evolving dynamics of the vehicular social network graph presents a small-world structure  ...  , where nodes tend to connect through clusters whose average distance is low on average.  ...  Best performance is with the SCARF technique, since the time-evolving dynamics of the vehicular social network graph allow forming a smallworld structure, where nodes tend to connect through clusters and  ... 
doi:10.1109/ccnc.2019.8651774 dblp:conf/ccnc/VegniLM19 fatcat:k3ip6jqhlvae7lc4r47jpxqqqa

Challenges on Probabilistic Modeling for Evolving Networks [article]

Jianguo Ding, Pascal Bouvry
2013 arXiv   pre-print
areas of network performance, network management and network security for evolving networks.  ...  This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application  ...  By using a DBN, we assume that dynamic data are generated sequentially by some hidden states of a dynamic factor evolving over time.  ... 
arXiv:1304.7820v2 fatcat:qvtskgtvpvh2npqbyjlghyu774

Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

Ran Jin, Chunhai Kou, Ruijuan Liu
2015 Cybernetics and Information Technologies  
Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms  ...  Therefore, we propose to leverage a clustering fusion method to improve the results of community detection.  ...  Though we consider different snapshots of evolving networks, we only use link based data for detecting communities.  ... 
doi:10.1515/cait-2015-0029 fatcat:elvhci2d35gwdoywfvq57il37y

Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition [chapter]

Nikola Kasabov, Kshitij Dhoble, Nuttapod Nuntalid, Ammar Mohemmed
2011 Lecture Notes in Computer Science  
This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network 'reservoirs' (epSNNr).  ...  The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal  ...  For the experiments, a software simulator of a epSNNr was developed using Brian software environment [25] .  ... 
doi:10.1007/978-3-642-24965-5_25 fatcat:iebez45tcrar7aciekcxo46xdy

Investigating The Performance Of Minimax Search And Aggregate Mahalanobis Distance Function In Evolving An Ayo/Awale Player

A. Randle O., Olugbara, O. O., M. Lall
2012 Zenodo  
In this paper we describe a hybrid technique of Minimax search and aggregate Mahalanobis distance function synthesis to evolve Awale game player.  ...  The hybrid technique helps to suggest a move in a short amount of time without looking into endgame database.  ...  The choice of Mahalanobis distance function as refinement procedure was motivated by the Probabilistic Distance Clustering (pd-clustering) technique [19] .  ... 
doi:10.5281/zenodo.1334147 fatcat:mp65g53q7zgnxksoavhrfvl2re

ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time

Yunpeng Cai, Yijun Sun
2011 Nucleic Acids Research  
To avoid exhaustive computation of pairwise distances between clusters, we represent each cluster of sequences as a probabilistic sequence, and define a set of operations to align these probabilistic sequences  ...  The new algorithm exhibits a quasilinear time and space complexity comparable to greedy heuristic clustering algorithms, while achieving a similar accuracy to the standard hierarchical clustering algorithm  ...  ACKNOWLEDGEMENTS The authors wish to thank Rob Knight for his thoughtful comments on the draft of this paper. The authors also would like to thank the editor Alan Kimmel and four  ... 
doi:10.1093/nar/gkr349 pmid:21596775 pmcid:PMC3152367 fatcat:7p5cwuyji5altegusu3wdxj3n4

Online Unsupervised Neural-Gas Learning Method for Infinite Data Streams [chapter]

Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd
2014 Advances in Intelligent Systems and Computing  
The method maintains a model as a dynamically evolving graph topology of data-representatives that we call neurons.  ...  Moreover, the proposed method performs a merging process which uses a distance-based probabilistic criterion to eventually merge neurons.  ...  parameters and evolves dynamically according to the data and the topology of neurons.  ... 
doi:10.1007/978-3-319-12610-4_4 fatcat:nkueoes5jzhr7gz6quo7yyox7i

Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

Umesh Kokate, Arvind Deshpande, Parikshit Mahalle, Pramod Patil
2018 Big Data and Cognitive Computing  
In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis.  ...  The performance of these data stream clustering algorithms is domain-specific and requires many parameters for density and noise thresholds.  ...  Probabilistic Model based clusters: Given data set, D, and k, the number of clusters required, that task of probabilistic model-based cluster analysis is to infer a set of k probabilistic clusters that  ... 
doi:10.3390/bdcc2040032 fatcat:7qw3oor66bbojcztnfb44gg3pi

Familiarising Probabilistic Distance Clustering System of Evolving Awale Player

Randle Oluwarotimi Abayomi
2012 International Journal on Applications of Graph Theory In wireless Ad Hoc Networks And sensor Networks  
This study developed a new technique based on Probabilistic Distance Clustering (PDC) for evolving Awale player and to compare its performance with that of a technique based on approximation of minimum  ...  The basic theory of pd-clustering is based on the assumption that the probability of an Euclidean point belonging to a cluster is inversely proportional to its distance from the cluster centroid.  ...  heuristic for evolving an Awale agent. ..... x x x x n .The general problem of data clustering is to partition a dataset into m clusters of similar data points.  ... 
doi:10.5121/jgraphoc.2012.4203 fatcat:sd2g2khcyvdhzbu2a66twcorcq

A probabilistic cellular automata model for highway traffic simulation

Marcelo Zamith, Regina Célia P. Leal-Toledo, Mauricio Kischinhevsky, Esteban Clua, Diego Brandão, Anselmo Montenegro, Edgar B. Lima
2010 Procedia Computer Science  
This work presents a probabilistic model for the microscopic simulation of traffic roads based on Nagel-Schreckenberg's model.  ...  The simulations developed and described herein give rise to a phase diagram which resembles and enriches the fundamental diagram, in its theoretical as well as for real data.  ...  A subsequent adjustment of each vehicle's speed considers its distance to the one immediately ahead. Slow-to-start models form a subset of the set of probabilistic models.  ... 
doi:10.1016/j.procs.2010.04.037 fatcat:j4g4oide7japzjtj32hopmoiei
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