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Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices

Francisco de A.T. de Carvalho, Yves Lechevallier, Filipe M. de Melo
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wcbukkuh2vbrpcbz2f4z2a2g6u" style="color: black;">Fuzzy sets and systems (Print)</a> </i> &nbsp;
This paper introduces fuzzy clustering algorithms that can partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices.  ...  These relevance weights change at each algorithm iteration and can either be the same for all fuzzy clusters or different from one fuzzy cluster to another.  ...  Partitioning Fuzzy K-Medoids Clustering Algorithms Based on Multiple Dissimilarity Matrices This section presents partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.fss.2012.09.011">doi:10.1016/j.fss.2012.09.011</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2rdnb7mn6zcenakqah7jpvz2gq">fatcat:2rdnb7mn6zcenakqah7jpvz2gq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811054216/http://www.cin.ufpe.br/~fatc/AM/DeCarvalho-Lechevallier-Melo-FSS-09-2010-R2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7a/45/7a453abf98cb7f949daf7cdee014f5b7f4e0023f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.fss.2012.09.011"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Quantitative Evaluation of Web user Session Dissimilarity measures using Medoids based Relational Fuzzy clustering

Dilip Singh Sisodia, Shrish Verma, Om Prakash Vyas
<span title="2016-07-26">2016</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
The quantitative performance evaluation of different session dissimilarity measures are performed using a relational fuzzy c-medoid clustering approach.  ...  The intra-cluster and inter-cluster distance based cluster quality ratio is used for performance evaluation.  ...  Figure 1 . 1 The VAT images of different augmented session dissimilarity matrices of 1341×1341.Then, multiple runs of fuzzy relational c-medoids clustering algorithm was performed with six dissimilarity  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9i28/89455">doi:10.17485/ijst/2016/v9i28/89455</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/glb2kov2xveyxn5dpknqexeqr4">fatcat:glb2kov2xveyxn5dpknqexeqr4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428153448/http://www.indjst.org/index.php/indjst/article/download/89455/72282" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9i28/89455"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Trimmed fuzzy clustering of financial time series based on dynamic time warping

Pierpaolo D'Urso, Livia De Giovanni, Riccardo Massari
<span title="2019-07-18">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kov3rwzipzf2dmxyohqwfpxrsm" style="color: black;">Annals of Operations Research</a> </i> &nbsp;
This result is achieved by combining the dissimilarity measures for each attribute by means of a weighting scheme, so as to obtain a distance measure for multiple attributes.  ...  A fuzzy clustering model for data with mixed features is proposed. The clustering model allows different types of variables, or attributes, to be taken into account.  ...  In this paper, we propose a novel fuzzy clustering algorithm, the Fuzzy C-Medoids clustering model for mixed data (FCMd-MD).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-019-03284-1">doi:10.1007/s10479-019-03284-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wt3zoqtmiba2jeweqlmkx7jipm">fatcat:wt3zoqtmiba2jeweqlmkx7jipm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106174912/https://iris.uniroma1.it/retrieve/handle/11573/1336412/1301531/Information%20Sciences%20%28D%27Urso%2c%20Masaari%2c%202019%29.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/da/83/da83751b1c355a13ac5df73680ae08ce52391788.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-019-03284-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

ECMdd: Evidential c -medoids clustering with multiple prototypes

Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-ga Liu
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
In this work, a new prototype-based clustering method named Evidential C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data, is proposed as an extension of Fuzzy  ...  C-Medoids (FCMdd) on the theoretical framework of belief functions.  ...  a generalized medoid-based Fuzzy clustering with Multiple Medoids (FMMdd) has been proposed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2016.05.005">doi:10.1016/j.patcog.2016.05.005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pvgwd2oxgzdmfnknh5n3ffwhry">fatcat:pvgwd2oxgzdmfnknh5n3ffwhry</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724102759/https://hal.archives-ouvertes.fr/hal-01326332/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/10/3110cddd2cea61684f29259e94e8d5558e4079bf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2016.05.005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Fault diagnosis on material handling system using feature selection and data mining techniques

M. Demetgul, K. Yildiz, S. Taskin, I.N. Tansel, O. Yazicioglu
<span title="">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vsb32i2geva2jpfusvm3s5wdsy" style="color: black;">Measurement (London)</a> </i> &nbsp;
Encoded signals were classified by using the Gustafson-Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm.  ...  In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems.  ...  Based on the norm-inducing matrices, the aim of the GK method, is obtained by minimizing the function J as Eq. (2).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.measurement.2014.04.037">doi:10.1016/j.measurement.2014.04.037</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gzmb3vsi3zfhpibsxxkfccjueq">fatcat:gzmb3vsi3zfhpibsxxkfccjueq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200323102237/https://isiarticles.com/bundles/Article/pre/pdf/46686.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/43/224384c2cd0ad849b364fdc17630daae46a046e9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.measurement.2014.04.037"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Adaptive Concept Learning through Clustering and Aggregation of Relational Data [chapter]

Hichem Frigui, Cheul Hwang
<span title="2007-04-26">2007</span> <i title="Society for Industrial and Applied Mathematics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/viwc2ys5x5a47ogpdlftfzj5fm" style="color: black;">Proceedings of the 2007 SIAM International Conference on Data Mining</a> </i> &nbsp;
CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously.  ...  Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings.  ...  PAM (Partitioning Around Medoids) [22] is another algorithm that is based on finding k representative objects (medoids) that minimize the sum of the within-cluster dissimilarities.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611972771.9">doi:10.1137/1.9781611972771.9</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sdm/FriguiH07.html">dblp:conf/sdm/FriguiH07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/naqfrkiguzgq7gz5nt2ghhx6km">fatcat:naqfrkiguzgq7gz5nt2ghhx6km</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130122121303/http://www.siam.org/proceedings/datamining/2007/dm07_009frigui.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/47/2b/472b82c0ede2ee8092be1b40b86af1874bb33929.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611972771.9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Cong Liu, Qianqian Chen, Yingxia Chen, Jie Liu
<span title="2019-01-17">2019</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
Recently a new clustering algorithm called 'multiobjective evolutionary clustering based on combining multiple distance measures' (MOECDM) was proposed to integrate Euclidean and Path distance measures  ...  Most of the existing clustering algorithms are often based on Euclidean distance measure.  ...  Shortly afterwards, Francisco proposed another two algorithms 'partitioning fuzzy Kmedoids clustering algorithm based on multiple dissimilarity matrices estimated locally and globally' (MFCMdd-RWG and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/3821025">doi:10.1155/2019/3821025</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6oujlvlvkvgyleqn7zqtafqnzq">fatcat:6oujlvlvkvgyleqn7zqtafqnzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502092900/http://downloads.hindawi.com/journals/mpe/2019/3821025.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/96/ac961ed67a3c90386e21c7ad3de4f9d2f59fc155.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/3821025"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Fuzzy clustering of physicochemical and biochemical properties of amino Acids

Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Dariusz Plewczynski
<span title="2011-10-13">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/spfkqu35dnd7rjl5ooxt2ihb4q" style="color: black;">Amino Acids</a> </i> &nbsp;
First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited.  ...  Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results.  ...  Genetic algorithm-based fuzzy c-medoids clustering GA-based fuzzy c-medoids (GAFCMdd) Maulik and Saha 2009; Maulik and Bandyopadhyay 2000) clustering algorithm also uses the same encoding policy as DEFCMdd  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00726-011-1106-9">doi:10.1007/s00726-011-1106-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21993537">pmid:21993537</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3397137/">pmcid:PMC3397137</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gsif2hruivbs3aufnru4isgzvi">fatcat:gsif2hruivbs3aufnru4isgzvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030102427/https://link.springer.com/content/pdf/10.1007%2Fs00726-011-1106-9.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7a/0a/7a0a036ba914eb65ca9ff35db24617f84a91f8cc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00726-011-1106-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3397137" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fuzzy c -ordered medoids clustering for interval-valued data

Pierpaolo D׳Urso, Jacek M. Leski
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach.  ...  This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID).  ...  Frigui and Krishnapuram [38] proposed a robust fuzzy clustering algorithm, termed robust C-prototypes algorithm, based on a generalization of the M-estimator to estimate the C prototypes and on the application  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2016.04.005">doi:10.1016/j.patcog.2016.04.005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hqmqf5n2mjbx7js4se2ryz72ay">fatcat:hqmqf5n2mjbx7js4se2ryz72ay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104070511/https://iris.uniroma1.it/retrieve/handle/11573/886554/264528/PATTERN%20RECOGNITION.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/61/41614784600bbbc3e213dc2159a3c0f068b9536e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2016.04.005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Pierpaolo D'Urso
<span title="2017-03-24">2017</span> <i title="Springer Nature"> Granular Computing </i> &nbsp;
Sato and Sato (1995) proposed a fuzzy clustering procedure for fuzzy data through an additive fuzzy clustering model based on a multiple criterion.  ...  based fuzzy clustering proposed by D'Urso et al. (2015), the exponential distance-based fuzzy clustering proposed by D'Urso et al. (2017), and the fuzzy c-ordered-medoids clustering for interval-valued  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41066-017-0040-y">doi:10.1007/s41066-017-0040-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fqbov5ba4zemvcu4kzhnqknkkm">fatcat:fqbov5ba4zemvcu4kzhnqknkkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030084106/https://link.springer.com/content/pdf/10.1007%2Fs41066-017-0040-y.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/f5/c3f540c6cd9dfe21a1a37123c9e5a2c7744a0c69.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41066-017-0040-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Partitioning hard clustering algorithms based on multiple dissimilarity matrices

Francisco de A.T. de Carvalho, Yves Lechevallier, Filipe M. de Melo
<span title="">2012</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices.  ...  These relevance weights change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another.  ...  matrix) as well as MRDCA (relational hard clustering algorithm that performs on multiple dissimilarity matrices) and CARD −R (relational fuzzy clustering algorithm that performs on multiple dissimilarity  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2011.05.016">doi:10.1016/j.patcog.2011.05.016</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fmrnc5eotzgwdepehqfqtsm7vu">fatcat:fmrnc5eotzgwdepehqfqtsm7vu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808214238/http://www-sop.inria.fr/axis/pages/bestpaper/DeCarvalho-Lechevallier-Melo-PR-09-2010-R1.dvi.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/4a/564a49359eda9f9b3212c68c2b3f23c16cb15ef6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2011.05.016"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Clustering of time series data—a survey

T. Warren Liao
<span title="">2005</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering  ...  results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters.  ...  Two counterparts for fuzzy partitions are the fuzzy c-means algorithm [4] and the fuzzy c-medoids algorithm [5] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2005.01.025">doi:10.1016/j.patcog.2005.01.025</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mu3ztrmhcffbzbbqafzmfbgyva">fatcat:mu3ztrmhcffbzbbqafzmfbgyva</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130509002727/http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.6594&amp;rep=rep1&amp;type=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/fa/20faa2ef4bb4e84b1d68750cda28d0a45fb16075.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2005.01.025"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Fuzzy clustering of fuzzy data based on robust loss functions and ordered weighted averaging

Pierpaolo D'Urso, Jacek M. Leski
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wcbukkuh2vbrpcbz2f4z2a2g6u" style="color: black;">Fuzzy sets and systems (Print)</a> </i> &nbsp;
In particular, in order to neutralize the negative effects of possible outlier fuzzy data in the clustering process, we proposed a robust fuzzy c-medoids clustering method for fuzzy data based on the combination  ...  In this paper, by considering the Partitioning Around Medoids (PAM) approach in a fuzzy framework, we propose a fuzzy clustering method for imprecise data formalized in a fuzzy manner.  ...  In particular, Sato and Sato [45] suggested a fuzzy clustering procedure for fuzzy data through an additive fuzzy clustering procedure based on multiple criteria. Hathaway et al.  ... 
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Cluster Analysis of Genomic Data [chapter]

K. S. Pollard, M. J. van der Laan
<span title="">2005</span> <i title="Springer New York"> Bioinformatics and Computational Biology Solutions Using R and Bioconductor </i> &nbsp;
We also show how to visualize a clustering result by plotting ordered dissimilarity matrices in R.  ...  We discuss statistical issues and methods in choosing the number of clusters, the choice of clustering algorithm, and the choice of dissimilarity matrix.  ...  Model based clustering algorithms are based on assuming that the vectors X i are i.i.d. from a mixture of distributions (e.g., a multivariate normal mixture).  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130601005123/http://www.cbcb.umd.edu:80/~hcorrada/CMSC858B/readings/Solutions_ch13.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/a9/a3a93919680cca19d602c158a546a391edfda1f4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/0-387-29362-0_13"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Time-series clustering – A decade review

Saeed Aghabozorgi, Ali Seyed Shirkhorshidi, Teh Ying Wah
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/epn2gc3fozcsphiz3xnhmwom7u" style="color: black;">Information Systems</a> </i> &nbsp;
With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract  ...  Clustering is a solution for classifying enormous data when there is not any early knowledge about classes.  ...  On the other hand, FCM (Fuzzy c-Means) algorithm [174, 175] and Fuzzy c-Medoids algorithm [176] build 'soft' clusters.  ... 
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