Results 201 to 210 of about 43,162 (235)

Efficient Markov clustering algorithm for protein sequence grouping

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
In this paper we propose an efficient reformulation of a Markov clustering algorithm, suitable for fast and accurate grouping of protein sequences, based on pairwise similarity information. The proposed modification consists of optimal reordering of rows and columns in the similarity matrix after every iteration, transforming it into a matrix with ...
László, Szilágyi, Sándor M, Szilágyi   +1 more
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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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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   +4 more
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Spectral clustering for non-reversible Markov chains

Computational and Applied Mathematics, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
K. Fackeldey, A. Sikorski, M. Weber
openaire   +2 more sources

Influence Maximization Algorithm Using Markov Clustering

2013
Social Network Services are known as a effective marketing platform in that the customers trust the advertisement provided by their friends and neighbors. Viral Marketing is a marketing technique that uses the pre-constructed social networks to perform maketing with small cost while maximizing the spread.
Chungrim Kim, Sangkeun Lee, Sungchan Park, Sang-goo Lee   +3 more
openaire   +1 more source

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 ...
A. Bookstein, S.T. Klein, T. Raita
openaire   +1 more source

Markov clustering ensemble

Knowledge-Based Systems, 2022
Luqing Wang, Junwei Luo, Hongjun Wang, Tianrui Li   +3 more
openaire   +1 more source

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 ...
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Constrained Spectral Clustering Using Absorbing Markov Chains

2012
Constrained spectral clustering (CSC) has recently shown great promise in improving clustering accuracy or catering for some specific grouping bias by encoding pairwise constraints into spectral clustering. Essentially, the existing CSC algorithms coarsely lie in two camps in terms of encoding pairwise constraints: (1) they modify the original ...
Jianyuan Li, Jihong Guan
openaire   +1 more source

Multi-Level Flow-Based Markov Clustering for Design Structure Matrices

Volume 7: 28th International Conference on Design Theory and Methodology, 2016
For decomposition and integration of systems one requires extensive knowledge on system structure. A Design Structure Matrix (DSM) can provide a simple, compact and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed.
Wilschut, T., Etman, P.L.F., Rooda, J.E., Adan, I.J.B.F.   +3 more
openaire   +3 more sources