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Estimating incremental dimensional algorithm with sequence data set
2013
2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
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This method avoids the need to compute the distance of each data object to the cluster center. It saves running time. ...
Hierarchical clustering is the grouping of objects of interest according to their similarity into a hierarchy, with different levels reflecting the degree of inter-object resemblance. ...
Each level of a dendrogram can be evaluated by a cluster validation method and the best level and its corresponding clusters are returned. HAC algorithms are non-parametric. ...
doi:10.1109/icprime.2013.6496461
fatcat:djw3tm7tkne4hme7svhjilppou
Understanding outside collaborations of the Chinese Academy of Sciences using Jensen-Shannon divergence
2009
Visualization and Data Analysis 2009
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Applying the approach to data on the outside collaborations of the Chinese Academy of Sciences and visualizing the results reveals interesting structure relevant for science policy decisions. ...
about how they collaborate with each other. ...
Since it is a metric, techniques wellfounded in a metric space, such as agglomerative hierarchical clustering with Ward's method, can be brought to bear. ...
doi:10.1117/12.812383
dblp:conf/vda/Duhon09
fatcat:nbrp6lpy45cnvmwipzs5mjygzi
Algorithms for hierarchical clustering: an overview, II
2017
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. ...
We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical densitybased approaches. ...
They also address the question of metrics: results are valid in a wide class of distances including those associated with the Minkowski metrics. ...
doi:10.1002/widm.1219
fatcat:4cdvfpypibe3petriqyuaiunk4
Happy and Immersive Clustering Segmentations of Biological Co-Expression Patterns
[article]
2024
arXiv
pre-print
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In this work, we present an approach for evaluating segmentation strategies and solving the biological problem of creating robust interpretable maps of biological data by employing wards agglomerative ...
Finally, we find that the cluster representations and label annotations, in the case with clusters of high immersiveness, correspond to compositionally inferred labels with the highest specificity. ...
Figure 3 : Synthetic data example employing agglomerative hierarchical clustering and complete linkage (maximum distance) method for cluster distance attribution. ...
arXiv:2402.06928v1
fatcat:2opogdohifckre2zc2x77wyicu
Ultrametric Component Analysis with Application to Analysis of Text and of Emotion
[article]
2013
arXiv
pre-print
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It is assumed that the data set, to begin with, is endowed with a metric, and we include discussion of how this can be brought about if a dissimilarity, only, holds. ...
The basis for part of the metric-endowed data set being ultrametric is to consider triplets of the observables (vectors). We develop a novel consensus of hierarchical clusterings. ...
Hierarchical agglomerative clustering algorithms are a general and widely-used class of algorithm for inducing an ultrametric on dissimilarity or distance input, or coordinate data on which a metric or ...
arXiv:1309.3611v1
fatcat:4sunhvuhwvcihgkp2nvspa73ua
Semi-supervised Hierarchical Clustering
2011
2011 IEEE 11th International Conference on Data Mining
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In this paper, we propose a novel semi-supervised hierarchical clustering framework based on ultra-metric dendrogram distance. ...
Semi-supervised clustering (i.e., clustering with knowledge-based constraints) has emerged as an important variant of the traditional clustering paradigms. ...
Based on the way the clusters are generated, clustering methods can be divided into two categories: partitional clustering and hierarchical clustering [2] [3] . ...
doi:10.1109/icdm.2011.130
dblp:conf/icdm/ZhengL11
fatcat:7kwlsml3u5gkjcjc5zxcoaw5z4
Semi-supervised Hierarchical Co-clustering
[chapter]
2012
Lecture Notes in Computer Science
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In this paper, we propose a novel semi-supervised hierarchical clustering framework based on ultra-metric dendrogram distance. ...
Semi-supervised clustering (i.e., clustering with knowledge-based constraints) has emerged as an important variant of the traditional clustering paradigms. ...
Based on the way the clusters are generated, clustering methods can be divided into two categories: partitional clustering and hierarchical clustering [2] [3] . ...
doi:10.1007/978-3-642-31900-6_39
fatcat:misnzmthgnevdlrxm7g3g5k2wi
Partially Supervised Speaker Clustering
2012
IEEE Transactions on Pattern Analysis and Machine Intelligence
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traditional speaker clustering methods based on the "bag of acoustic features" representation and statistical model based distance metrics, 2) our advocated use of the cosine distance metric yields consistent ...
Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform ...
These two metrics are standard for evaluating (general) data clustering results [42] . ...
doi:10.1109/tpami.2011.174
pmid:21844626
fatcat:g7m7wki6pvcb3gotxydj4k6ewq
A Survey of Partitional and Hierarchical Clustering Algorithms
[chapter]
2018
Data Clustering
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K-modes is a non-parametric clustering algorithm suitable for handling categorical data and optimizes a matching metric (L 0 loss function) without using any explicit distance metric. ...
Ward's method chooses the initial centroids by using the sum of squared errors to evaluate the distance between two clusters. ...
doi:10.1201/9781315373515-4
fatcat:nv3tftuhyzcbdfi6g7invscl5u
Improving Test Distance for Failure Clustering with Hypergraph Modelling
[article]
2021
arXiv
pre-print
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We introduce a new test distance metric based on hypergraphs and evaluate their accuracy using multi-fault benchmarks that we have built on top of Defects4J and SIR. ...
Results show that our technique, Hybiscus, can automatically achieve perfect clustering (i.e., the same number of clusters as the ground truth number of root causes, with all failing tests with the same ...
Our empirical evaluation shows that, when used with Agglomerative Hierarchical Clustering (AHC) and a distance-based estimation of cluster numbers, Hybiscus can significantly outperform other failure clustering ...
arXiv:2104.10360v1
fatcat:cfa7r6wsonaphcwzoif5hxp5fy
Meta Clustering
2006
IEEE International Conference on Data Mining. Proceedings
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We present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. ...
We evaluate meta clustering on four test problems and two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings. ...
Pedro Artigas, Anna Goldenberg, and Anton Likhodedov helped with early experiments in meta clustering as part of a class project at CMU. ...
doi:10.1109/icdm.2006.103
dblp:conf/icdm/CaruanaENS06
fatcat:t7fij6li3rdmhh23ulwwkx7yfq
Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study
[chapter]
2020
Lecture Notes in Computer Science
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We compare the results of many well-known clustering algorithms such ask-means, HDBSCAN, GMM and Agglomerative Hierarchical Clustering when they operate on the low-dimension feature space yielded by UMAP ...
A series of experiments on several image datasets demonstrate that the proposed method allows each of the clustering algorithms studied to improve its performance on each dataset considered. ...
Evaluation Metrics In order to validate the performance of unsupervised clustering algorithms, we use the two standard evaluation metrics, accuracy (ACC) and Normalized Mutual Information (NMI). ...
doi:10.1007/978-3-030-51935-3_34
fatcat:6yrc4jamwne7nhisg5mod4k3te
Russian News Clustering and Headline Selection Shared Task
[article]
2021
arXiv
pre-print
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As a part of it, we propose the tasks of Russian news event detection, headline selection, and headline generation. These tasks are accompanied by datasets and baselines. ...
This paper presents the results of the Russian News Clustering and Headline Selection shared task. ...
Acknowledgements We would like to thank the participants of all three tracks, especially Tatiana Shavrina, Ivan Bondarenko, and Nikita Yudin for helpful comments and valuable suggestions. ...
arXiv:2105.00981v3
fatcat:6oiewmaj7rd37ephovhlxeufsu
Identification and Investigation of the User Session for Lan Connectivity Via Enhanced Partition Approach of Clustering Techniques
2012
International Journal of Computer Science Engineering and Information Technology
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This paper mainly presents some technical discussions on the identification and analyze of "LAN usersessions". The identification of a user-session is non trivial. ...
We have defined a clustering based approach in detail, and also we discussed positive and negative of this approach, and we apply it to real traffic traces. ...
To efficiently position, in our unidimensional metric space, the representatives at procedure start-up, we evaluate the distance between any two adjacent samples .According to the distance metric, we take ...
doi:10.5121/ijcseit.2012.2604
fatcat:6qkz4b7z7vhfhouftjgkcusiqa
KL divergence based agglomerative clustering for automated Vitiligo grading
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This leads to a very powerful yet elegant method for bottomup agglomerative clustering with strong theoretical guarantees. ...
We introduce albedo and reflectance fields as features for the distance computations. We compare against other established methods to bring out possible pros and cons of the proposed method. ...
A number of works, in the recent past, present agglomerative schemes for clustering with exponential families. ...
doi:10.1109/cvpr.2015.7298886
dblp:conf/cvpr/GuptaSMA15
fatcat:os3gvi6efvg3xgljdclfkzmnwa
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