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Stability and Performances in Biclustering Algorithms [chapter]

Maurizio Filippone, Francesco Masulli, Stefano Rovetta
2009 Lecture Notes in Computer Science  
Stability in clustering may be related to clustering quality or ensemble diversity, and therefore used in several ways to achieve a deeper understanding or better confidence in bioinformatic data analysis  ...  In the specific field of fuzzy biclustering, stability can be analyzed by porting the definition of existing stability indexes to a fuzzy setting, and then adapting them to the biclustering problem.  ...  Acknowledgments We thank Luca Zini for programming support and Giorgio Valentini for the useful suggestions provided while discussing this work.  ... 
doi:10.1007/978-3-642-02504-4_8 fatcat:vmartnppdfaq3ftoafyc7yoje4

Developing Additive Spectral Approach to Fuzzy Clustering [chapter]

Boris G. Mirkin, Susana Nascimento
2011 Lecture Notes in Computer Science  
An additive spectral method for fuzzy clustering is presented. The method operates on a clustering model which is an extension of the spectral decomposition of a square matrix.  ...  The computation proceeds by extracting clusters one by one, which allows us to draw several stopping rules to the procedure.  ...  This work has been supported by grant PTDC/EIA/ 69988/2006 from the Portuguese Foundation for Science & Technology.  ... 
doi:10.1007/978-3-642-21881-1_43 fatcat:pnnjxrunejfv5mouhulc2xgroy

A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes [article]

Kaijie Xu, Witold Pedrycz, Zhiwu Li, Yinghui Quan, Weike Nie
2020 arXiv   pre-print
Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions.  ...  Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters.  ...  For the Fuzzy C-Means clustering, the fuzziness coefficient κ is set as 2, which is the most frequently used value in practice.  ... 
arXiv:2005.05519v1 fatcat:lzwegn6trvbkrf5at436ulg6qi

A Hybrid Cluster-Lift Method for the Analysis of Research Activities [chapter]

Boris Mirkin, Susana Nascimento, Trevor Fenner, Luís Moniz Pereira
2010 Lecture Notes in Computer Science  
A hybrid of two novel methods -additive fuzzy spectral clustering and lifting method over a taxonomy -is applied to analyse the research activities of a department.  ...  Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions.  ...  Igor Guerreiro is acknowledged for developing software for the ESSA tool. Rui Felizardo is acknowledged for developing software for the lifting algorithm with interface shown in Figure 1 .  ... 
doi:10.1007/978-3-642-13769-3_19 fatcat:niipcg3p4nfpjh5icgaobfzoye

Dual Tree Complex Wavelet Transform, Probabilistic Neural Network and Fuzzy Clustering based on Medical Images Classification – A Study

Rajesh Sharma R, Akey Sungheetha
2018 International Journal of Advanced engineering Management and Science  
It is a programmed structure for phase classification using learning mechanism and to sense the Brain Tumor through spatial fuzzy clustering methods for bio medical applications.  ...  Our proposal employs a segmentation technique, Spatial Fuzzy Clustering Algorithm, for segmenting MRI images to diagnose the Brain Tumor in its earlier phase for scrutinizing the anatomical makeup.  ...  -eq (2) Entropy: It quantifies image texture degree of randomness, the space co-occurrence matrix for all values are equivalent then it realizes the minimum value in equation 3.  ... 
doi:10.22161/ijaems.4.12.2 fatcat:tw2ezejx5bckjmclidije2xbea

Rule generation for hierarchical collaborative fuzzy system

Paulo Salgado
2008 Applied Mathematical Modelling  
For this we use a set of methods: Cluster Rule Generation; Recursive Least-Square; Data Rejection Algorithm and Backward Fuzzy Rule Selection algorithm.  ...  Other authors [23] [24] [25] have proposed clustering algorithms to obtain fuzzy rules from the given input-output data.  ...  sum of square errors by exclusion of the kth point.  ... 
doi:10.1016/j.apm.2007.03.007 fatcat:ojef7nnqknbnzloebpjyn3ygtu

Identification of fuzzy relational models for fault detection

P. Amann, J.M. Perronne, G.L. Gissinger, P.M. Frank
2001 Control Engineering Practice  
In this paper, the identi"cation of fuzzy models for residual generation is discussed.  ...  This paper presents the concept of fuzzy relational models for use in a fuzzy output estimator.  ...  Here, the application of a clustering algorithm may be useful, such as, for example, the fuzzy clustering algorithm proposed by Kroll (1997) .  ... 
doi:10.1016/s0967-0661(01)00016-8 fatcat:n5iwds4z7jbb5lnvxvk3tkliqa

Co-Clustering-Based Clustering and Segmentation for Pattern Discovery from Time Course Data

Hyuk Cho
2014 International Journal of Information and Electronics Engineering  
He is known for his work on co-clustering algorithms, their extensions, and their applications to various practical tasks in real world problems.  ...  Clustering similarity performance among k-means, one existing co-clustering, and the two proposed clustering segmentation algorithms is compared. .  ...  Minimum-Sum Squared Residue Co-Clustering The residue matrix H leads to the following objective function for minimizing squared residues: find both row and column clusters simultaneously such that ∥H∥  ... 
doi:10.7763/ijiee.2014.v4.464 fatcat:ps3vhd5cynecfk7onvphhpwzkq

Constructing and Mapping Fuzzy Thematic Clusters to Higher Ranks in a Taxonomy [chapter]

Boris Mirkin, Susana Nascimento, Trevor Fenner, Luís Moniz Pereira
2010 Lecture Notes in Computer Science  
The profiles are then generalized in two steps: first, by fuzzy clustering, and then by mapping the clusters to higher ranks of the taxonomy.  ...  We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles.  ...  Igor Guerreiro is acknowledged for developing software for the ESSA tool. Rui Felizardo is acknowledged for developing software for the lifting algorithm with interface shown in Figures 5.  ... 
doi:10.1007/978-3-642-15280-1_31 fatcat:xuzgk52gp5aeljocmjjusko3j4

A Hybrid Algorithm for Classification of Compressed ECG

Shubhada S. Ardhapurkar, Ramandra R. Manthalkar, Suhas S. Gajre
2012 International Journal of Information Technology and Computer Science  
Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database.  ...  Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.  ...  The concerned co-ordinates of cluster center were updated.  ... 
doi:10.5815/ijitcs.2012.02.04 fatcat:223wjqgqfba6db2urbbchwhcia

Fuzzy clustering of time series gene expression data with cubic-spline

Yu Wang, Maia Angelova, Akhtar Ali
2013 Journal of Biosciences and Medicines  
by applying fuzzy cmeans clustering on the resulting splines (FCMS).  ...  Data clustering techniques have been applied to extract information from gene expression data for two decades.  ...  − ∑ ∫ (12) The first term of Eq.12 is residual sum of squares, which quantifies the closeness to gene expression data points, and the second term, is the integrated squared second derivative, which quantifies  ... 
doi:10.4236/jbm.2013.13004 fatcat:fuzl7bgzxbdslmlbwlkbb332xy

FAMACRO: Fuzzy and Ant Colony Optimization Based MAC/Routing Cross-layer Protocol for Wireless Sensor Networks

Sachin Gajjar, Mohanchur Sarkar, Kankar Dasgupta
2015 Procedia Computer Science  
FAMACRO uses fuzzy logic with residual energy, number of neighboring nodes and quality of communication link as input variables for cluster head selection.  ...  This paper presents Fuzzy and Ant Colony Optimization (ACO) based MAC/Routing cross-layer protocol (FAMACRO) for Wireless Sensor Networks that encompases cluster head selection, clustering and inter-cluster  ...  It avoids "hot spots" by unequal clustering and uses fuzzy logic for cluster head selection and ACO for inter-cluster routing.  ... 
doi:10.1016/j.procs.2015.01.012 fatcat:7hgk26edk5ftncpxjkbtc4aex4

A rule based fuzzy model for the prediction of petrophysical rock parameters

Jose Finol, Yi Ke Guo, Xu Dong Jing
2001 Journal of Petroleum Science and Engineering  
In this approach, a fuzzy clustering algorithm is combined with the least-square approximation method to identify the structure and parameters of the fuzzy model from sets of numerical data.  ...  The rule-based fuzzy model corresponds to the Takagi-Sugeno-Kang method of fuzzy reasoning proposed by Sugeno and his co-authors.  ...  The authors wish to express their gratitude to Ignacio Torres, German Maldonado, Leida Abreu and Miguel Jakymec for their valuable assistance. We are also in debt with Dr.  ... 
doi:10.1016/s0920-4105(00)00096-6 fatcat:shiuol7pyzbk7jxukbsxs274fm

Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

Osman Taylan, Abdulaziz S. Alkabaa, Mohammed Alamoudi, Abdulrahman Basahel, Mohammed Balubaid, Murad Andejany, Hisham Alidrisi
2021 Atmosphere  
In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN.  ...  Several important pollutants, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have important effects on air quality.  ...  , the minimum error was obtained by the mean square error approach.  ... 
doi:10.3390/atmos12060713 fatcat:xo4ltbayjnbi3m6iua5qylss5u

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

Mara L Hartsperger, Florian Blöchl, Volker Stümpflen, Fabian J Theis
2010 BMC Bioinformatics  
overlapping (fuzzy) clusters.  ...  Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.  ...  Instead of squared norm minimization of the residuals D (ij) , a higher residual power is minimized, which results in overlapping non-trivial cluster assignments.  ... 
doi:10.1186/1471-2105-11-522 pmid:20961418 pmcid:PMC3247861 fatcat:rgjpqq6lzbhjnaf55ubvflkjfq
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