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Self-Tuning Clustering: An Adaptive Clustering Method for Transaction Data
[chapter]
2002
Lecture Notes in Computer Science
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In essence, clustering is meant to divide a set of data items into some proper groups in such a way that items in the same group are as similar to one another as possible. ...
Clearly, the smaller the SL ratio of a cluster, the more similar to one another the items in the cluster are. ...
The authors in [7] proposed an algorithm by using large items as the similarity measurement to divide the transactions into clusters such that similar transactions are in the same clusters. ...
doi:10.1007/3-540-46145-0_5
fatcat:mrpyihju5be4xgngoi7enz5ivm
Clustering of gene expression data: performance and similarity analysis
2006
BMC Bioinformatics
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By using our data mining tool, Cluster Diff, it is possible to analyze the similarity of clusters generated by different algorithms and thereby enable comparisons of different clustering methods. ...
We then introduce Cluster Diff, a new data mining tool, to conduct the similarity analysis of clusters generated by different algorithms. ...
Acknowledgements We would like to thank Dr. Dong-Guk Shin and Dr. Jae-guon Nam at the Univ. of Connecticut for providing the software for cluster similarity analysis in this work. We also thank Dr. ...
doi:10.1186/1471-2105-7-s4-s19
pmid:17217511
pmcid:PMC1780119
fatcat:quuvzhle4fbdve7vs224yllza4
Clustering of Gene Expression Data: Performance and Similarity Analysis
2006
First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06)
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By using our data mining tool, Cluster Diff, it is possible to analyze the similarity of clusters generated by different algorithms and thereby enable comparisons of different clustering methods. ...
We then introduce Cluster Diff, a new data mining tool, to conduct the similarity analysis of clusters generated by different algorithms. ...
Acknowledgements We would like to thank Dr. Dong-Guk Shin and Dr. Jae-guon Nam at the Univ. of Connecticut for providing the software for cluster similarity analysis in this work. We also thank Dr. ...
doi:10.1109/imsccs.2006.43
dblp:conf/imsccs/YinH06
fatcat:v4nrmix3snagjouvnx7ow5hxcm
Instigating Self Organizing Map with Linear Neurons for Effective Gaussian Clustering
2018
IJIREEICE
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When the number of SOM units is large, to simplify quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. ...
Self-organizing maps are known for its clustering, visualization and classification capabilities. ...
Factor 4: Clustering Clustering groups based of their mutual similarities. Clustering achieves high within cluster similarity and low inter-cluster similarity. ...
doi:10.17148/ijireeice.2018.6104
fatcat:wjohuzupdfexhe44fybxytuwie
Near-duplicate video detection based on an approximate similarity self-join strategy
2016
2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)
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Nonetheless, methods for similarity self-join have poor performance when applied to highdimensional data. ...
Our strategy is based on clustering techniques to find out groups of videos which are similar to each other. ...
ACKNOWLEDGMENTS The authors are grateful to FAPEMIG/INRIA/MOTIF, CNPq, CAPES and STIC-AmSud for the partial financial support of this work. ...
doi:10.1109/cbmi.2016.7500278
dblp:conf/cbmi/SilvaPGAAG16
fatcat:yzmm7vvvavazzk4znqhqvhravm
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.02134v1 fatcat:qlidx5gyhzel7ooq4hycrbufau
We apply KDS to the unsupervised clustering problem and prove theoretical performance guarantees. ...
Experiments show that the algorithm is highly efficient and performs competitively on synthetic and real data sets. ...
Ultimately, these two limitations make it difficult to reap the benefits of sparse self-representation when constructing a similarity graph on large data sets with nonlinear structure. ...
arXiv:2012.02134v2
fatcat:jw7nfmtztra7hcg2nhj7457ucu
Text Clustering Algorithms: A Review
2014
International Journal of Computer Applications
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Clustering is an important part of the data mining. Clustering is the process of dividing the large &similar type of text into the same class. ...
This data is in unstructured format which makes it tedious to analyze it, so we need methods and algorithms which can be used with various types of text formats. ...
COMPARISION Clustering is the process to divide the data sets into the similar group. ...
doi:10.5120/16946-7075
fatcat:op3xwjavtraehgknfkf3hidcqy
CLASSIFICATION OF TEXT DATA USING FEATURE CLUSTERING ALGORITHM
2014
International Journal of Research in Engineering and Technology
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In this paper, we propose a feature clustering algorithm for classifying the text data. The document set contains number of words; these words are grouped into clusters based on the similarity. ...
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. Generally clustering means the collection of similar objects or data in groups. ...
Self-Constructing Clustering In this module, we use the self-constructing clustering algorithm. First we read each word pattern, then we compare the similarity based on the original words. ...
doi:10.15623/ijret.2014.0315062
fatcat:iwuxma6gsvgdtpfuzxbfty2xaq
Clustering Using Adaptive Self-organizing Maps (ASOM) and Applications
[chapter]
2005
Lecture Notes in Computer Science
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Applications of the resulting software to clustering biological data are discussed in detail. ...
Like the traditional SOMs, this clustering technique also provides useful information about the relationship between the resulting clusters. ...
If, for the given data set, a priori grouping information is available, then entropy can be used to evaluate the clustering results. ...
doi:10.1007/11428848_120
fatcat:xrj5uncddnbitb45ptmasnplki
"GeoPlot"
2002
Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02
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In Informedia [14], using a gazetteer on news video clips, we map news onto points on the globe and find correlations between sets of points. ...
The proposed tool is "Geo-Plot", which is fast to compute and gives a lot of useful information which traditional text retrieval can not find. ...
(Result 3) We showed how to use GeoPlots to discover • Clusters and intrinsic dimension Using self-plot, we can determine whether a set of points (places) is clustered (plateaus in the self-plot), or ...
doi:10.1145/584857.584859
fatcat:afxyunb6v5a3berh7hjhvvfmoi
"GeoPlot"
2002
Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02
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In Informedia [14], using a gazetteer on news video clips, we map news onto points on the globe and find correlations between sets of points. ...
The proposed tool is "Geo-Plot", which is fast to compute and gives a lot of useful information which traditional text retrieval can not find. ...
(Result 3) We showed how to use GeoPlots to discover • Clusters and intrinsic dimension Using self-plot, we can determine whether a set of points (places) is clustered (plateaus in the self-plot), or ...
doi:10.1145/584792.584859
dblp:conf/cikm/PanF02
fatcat:sgltoxgjh5a2hnokesbhyctfiq
Visualization of Temporal Similarity in Field Data
2012
IEEE Transactions on Visualization and Computer Graphics
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ACKNOWLEDGMENTS The authors would like to thank the German Research Foundation (DFG) for supporting the project within the Cluster of Excellence in Simulation Technology (EXC 310/1) and the Collaborative ...
Fig. 9 . 9 Visualizing self-similarity (a) and clusters (b) in the von Kármán data set. ...
Furthermore, the size of the data sets is limited by GPU memory for rendering the spatio-temporal clusters, and for large data sets computing the similarity information required for clustering is an expensive ...
doi:10.1109/tvcg.2012.284
pmid:26357108
fatcat:rpfmlgqlyrawtgd6fw3er4tyby
Clustering by soft-constraint affinity propagation: applications to gene-expression data
2007
Bioinformatics
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We show that the algorithm efficiently unveils the hierarchical cluster structure present in the data sets. Further on, it allows to extract sparse gene expression signatures for each cluster. ...
Motivation: Similarity-measure based clustering is a crucial problem appearing throughout scientific data analysis. ...
M.L. would like to thank the Malawi Polytechnic for hospitality during the preparation of the manuscript. ...
doi:10.1093/bioinformatics/btm414
pmid:17895277
fatcat:ukuvf4z4xncr7oqtk3ulg6ggye
A Review on Clustering Technique
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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Large data set have many hidden pattern which have very crucial information, Clustering is such technique which find the hidden pattern from the large data. ...
Competitive learning is used for Clustering in Neural network. Example of Competitive learning, SOM and ART are famous for clustering. ...
Because of the similarity measure it uses to determine the data items to be compressed, BIRCH only performs well on data sets with spherical clusters. ...
doi:10.17762/ijritcc2321-8169.1503136
fatcat:rdt7mpmyvnbm3ph6o7v72wmpdy
A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering
2005
International Journal of Fuzzy Logic and Intelligent Systems
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In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. ...
The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. ...
We extended the learning method of clustering from unsupervised learning to supervised learning using input-output relation as a neuro-fuzzy model. ...
doi:10.5391/ijfis.2005.5.2.095
fatcat:43yama2575fsbgtzjdgewriyti
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