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Geometrical inspired pre-weighting enhances Markov clustering community detection in complex networks [PDF]
Applied Network Science, 2021 Markov clustering is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space. However, its community detection performance in complex networks has been demonstrating results far from the state of the ...
Claudio Durán +2 more
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Generalization of Markov Diophantine Equation via Generalized Cluster Algebra [PDF]
The Electronic Journal of Combinatorics, 2023 In this paper, we deal with two classes of Diophantine equations, $x^2+y^2+z^2+k_3xy+k_1yz+k_2zx=(3+k_1+k_2+k_3)xyz$ and $x^2+y^4+z^4+2xy^2+ky^2z^2+2xz^2=(7+k)xy^2z^2$, where $k_1,k_2,k_3,k$ are nonnegative integers. The former is known as the Markov Diophantine equation if $k_1=k_2=k_3=0$, and the latter is a Diophantine equation recently studied by ...
Yasuaki Gyoda, Kodai Matsushita
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Dynamic order Markov model for categorical sequence clustering [PDF]
Journal of Big Data, 2021 Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data.
Rongbo Chen +4 more
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Clustering metagenomic sequences with interpolated Markov models [PDF]
BMC Bioinformatics, 2010 Background Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods.
Kelley David R, Salzberg Steven L
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HTTP Traffic Graph Clustering using Markov Clustering Algorithm
International Journal of Computer Applications, 2014 Graph-based techniques and analysis have been used for IP network traffic analysis. The objective of this paper is to study the hosts’ interaction behavior and use graph clustering algorithm, the Markov clustering algorithm, to group (cluster) hosts which have interaction using the HTTP protocol.
Yessica Nataliani, Theophilus Wellem
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PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks [PDF]
BMC Bioinformatics, 2019 Background How can we obtain fast and high-quality clusters in genome scale bio-networks? Graph clustering is a powerful tool applied on bio-networks to solve various biological problems such as protein complexes detection, disease module detection, and ...
Yongsub Lim +4 more
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A Markov Prediction Model Based on Page Hierarchical Clustering
International Journal of Distributed Sensor Networks, 2009 Yao Yao, Lei Shi, Zhanhong Wang
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Clustering in Block Markov Chains [PDF]
The Annals of Statistics, 2020 This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the $n$ possible states are divided into a finite number of $K$ groups or clusters, such that states in the same cluster exhibit the same transition rates to other states.
Sanders, Jaron +2 more
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Object Type Clustering Using Markov Directly-Follow Multigraph in Object-Centric Process Mining
IEEE Access, 2022 Object-centric process mining is a new process mining paradigm with more realistic assumptions about underlying data by considering several case notions, e.g., an order handling process can be analyzed based on order, item, package, and route case ...
Amin Jalali
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Bayesian clustering for continuous‐time hidden Markov models [PDF]
Canadian Journal of Statistics, 2021 We develop clustering procedures for longitudinal trajectories based on a continuous‐time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically, in this article we carry out finite and infinite mixture model‐based clustering for a CTHMM and achieve inference using Markov chain Monte Carlo (MCMC).
Yu Luo +2 more
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