Results 221 to 230 of about 43,679 (257)

Operon prediction by Markov clustering

International Journal of Data Mining and Bioinformatics, 2014
The prediction of operons is a critical step for the reconstruction of biochemical and regulatory networks at the whole genome level. In this paper, a novel operon prediction model is proposed based on Markov Clustering (MCL). The model employs a graph-clustering method by MCL for prediction and does not need a classifier.
W. Du, Z. Cao, Y. Wang, Blanzieri, Enrico, C. Zhang, Y. Liang   +5 more
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Markov models for clusters in concordance compression

Proceedings of IEEE Data Compression Conference (DCC'94), 2002
An earlier paper developed a procedure for compressing concordances, assuming that all elements occurred independently. In this paper, the earlier models are extended to take the possibility of clustering into account. The authors suggest several models adapted to concordances of large full-text information retrieval systems, which are generally ...
Abraham Bookstein, Shmuel T. Klein, Timo Raita   +2 more
openaire   +1 more source

On Clusters in Markov Chains

2006
Motivated by the computational difficulty of analyzing very large Markov chains, we define a notion of clusters in (not necessarily reversible) Markov chains, and explore the possibility of analyzing a cluster “in vitro,” without regard to the remainder of the chain.
Nir Ailon, Steve Chien, Cynthia Dwork
openaire   +1 more source

Subspace distribution clustering hidden Markov model

IEEE Transactions on Speech and Audio Processing, 2001
Most contemporary laboratory recognizers require too much memory to run, and are too slow for mass applications. One major cause of the problem is the large parameter space of their acoustic models. In this paper, we propose a new acoustic modeling methodology which we call subspace distribution clustering hidden Markov modeling (SDCHMM) with the aim ...
Enrico Bocchieri, Brian Kan-Wing Mak
openaire   +2 more sources

Sparse hidden Markov models for purer clusters

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
The hidden Markov model (HMM) is widely popular as the de facto tool for representing temporal data; in this paper, we add to its utility in the sequence clustering domain - we describe a novel approach that allows us to directly control purity in HMM-based clustering algorithms.
Sujeeth Bharadwaj, Mark Hasegawa-Johnson, Jitendra Ajmera, Om Deshmukh, Ashish Verma 0001   +4 more
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A New Markov Model for Clustering Categorical Sequences

2011 IEEE 11th International Conference on Data Mining, 2011
Clustering categorical sequences remains an open and challenging task due to the lack of an inherently meaningful measure of pair wise similarity between sequences. Model initialization is an unsolved problem in model-based clustering algorithms for categorical sequences.
Tengke Xiong, Shengrui Wang, Qingshan Jiang, Joshua Zhexue Huang   +3 more
openaire   +1 more source

Clustering-based Mode Reduction for Markov Jump Systems

2022
While Markov jump systems (MJSs) are more appropriate than LTI systems in terms of modeling abruptly changing dynamics, MJSs (and other switched systems) may suffer from the model complexity brought by the potentially sheer number of switching modes. Much of the existing work on reducing switched systems focuses on the state space where techniques such
Du, Zhe, Ozay, Necmiye, Balzano, Laura
openaire   +3 more sources

Clustering of Bursts of Openings in Markov and Semi-Markov Models of Single Channel Gating

Advances in Applied Probability, 1997
The gating mechanism of a single ion channel is usually modelled by a continuous-time Markov chain with a finite state space. The state space is partitioned into two classes, termed ‘open’ and ‘closed’, and it is possible to observe only which class the process is in. In many experiments channel openings occur in bursts.
Ball, Frank, Davies, Sue
openaire   +2 more sources

Ranking document clusters using markov random fields

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013
An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types of cluster-relevance evidence; e.g., the query-similarity values of the cluster's documents and query-
Fiana Raiber, Oren Kurland
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Markov Blanket Approximation Based on Clustering

2011
This paper presents new idea for Markov blanket approximation. It uses well known heuristic ordering of variables based on mutual information, but in another way then it was considered in previous works. Instead of using it as a simple help tool in a more complicated method most often based on statistical tests - presented here idea tries to rely ...
openaire   +1 more source