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Automatic Subspace Clustering of High Dimensional Data

Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan
2005 Data mining and knowledge discovery  
In the recent years, clustering of data in large data sets has become a rallying area of research and it is gaining momentum.  ...  Clustering analysis is a vibrant field of research intelligence and data mining.  ...  The main requirements considered are their ability to identify clusters embedded in subspaces. The subspaces contain high dimensional data and scalability.  ... 
doi:10.1007/s10618-005-1396-1 fatcat:t527k7b24vcu5mqtvwwxp3lqsi

Automatic subspace clustering of high dimensional data for data mining applications

Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan
1998 Proceedings of the 1998 ACM SIGMOD international conference on Management of data - SIGMOD '98  
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility  ...  We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality.  ...  CLIQUE automatically finds subspaces with high-density clusters.  ... 
doi:10.1145/276304.276314 dblp:conf/sigmod/AgrawalGGR98 fatcat:wgffokwnm5db5hdrnohjj3j3la

An Efficient Density Parameter-Light in Enhanced Subspace Clustering in High Dimensional Data

Rama Devi Jujjuri, M.Venkateswara Rao
2019 International Journal of Advanced Science and Technology  
Subspace clustering identifies the clusters stored in subspaces of a high dimensional dataset.  ...  Further, as high dimensional data has converted more and more prevalent in real-world applications due to the advances of vast data technologies.  ...  First, mine the significant subspace quality clusters in the high dimensional data.  ... 
doi:10.33832/ijast.2019.128.04 fatcat:ht4olz2o2vcujbyixbndnbdwwi

A method for automatically determining The number of clusters of LAC↑

Han Liu, Qingfeng Wu, Huailin Dong, Shuangshuang Wang, Qing Cai, Zhuo Ma
2009 2009 4th International Conference on Computer Science & Education  
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features.  ...  Experiments have shown that the improved LAC could search for the true number of clusters in high dimensional data sets automatically, as well as elevation of its clustering accuracy.  ...  High dimensional data, such as text data and gene expression data, bears the trait of high dimensionality and data sparsity.  ... 
doi:10.1109/iccse.2009.5228241 fatcat:7aymhd3cvfe6xe3f657seycprm

Subspace search and visualization to make sense of alternative clusterings in high-dimensional data

Andrada Tatu, Fabian Maas, Ines Farber, Enrico Bertini, Tobias Schreck, Thomas Seidl, Daniel Keim
2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)  
In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data.  ...  So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive methods aim to provide effective visual mappings to show, relate, and navigate HD  ...  ACKNOWLEDGEMENTS The research leading to these results has received funding from the "SteerSCiVA: Steerable Subspace Clustering for Visual Analytics" DFG-664/11 project.  ... 
doi:10.1109/vast.2012.6400488 dblp:conf/ieeevast/TatuMFBSSK12 fatcat:mfgt3jde2jf4jie4g6ngqmenf4

Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data

Jiwu Zhao, Stefan Conrad
2013 Proceedings of the 2nd International Conference on Data Technologies and Applications  
Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data.  ...  Subspace clustering is an extension of traditional clustering that enables finding clusters in subspaces within a data set, which means subspace clustering is more suitable for detecting clusters in high-dimensional  ...  Subspace clustering is usually applied in high-dimensional data sets. Many famous subspace clustering algorithms can find clusters in subspaces of the data set.  ... 
doi:10.5220/0004486600140022 dblp:conf/data/ZhaoC13 fatcat:sewnvhur6vcbnb2pgzc7sbialq

Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data

Fangfang Zhou, Juncai Li, Wei Huang, Ying Zhao, Xiaoru Yuan, Xing Liang, Yang Shi
2016 2016 IEEE Pacific Visualization Symposium (PacificVis)  
Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process.  ...  In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information.  ...  ACKNOWLEDGEMENTS This work is supported by the National Natural Science Foundation of China under Grant Nos. 61103108, 61170204 and 61402540. (video demo for this paper:  ... 
doi:10.1109/pacificvis.2016.7465260 dblp:conf/apvis/ZhouLHZYLS16 fatcat:c5tpgfxqbzcnrdjg67utumdyoe

Multivariate volume visualization through dynamic projections

Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci
2014 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV)  
We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views  ...  Figure 1 : An example of the semi-automatic transfer function (TF) design for Hurricane Isabel dataset. Left: subspace view navigation panel.  ...  Given a reduced dataset, we apply subspace clustering [23] to represent the high-dimensional attribute space as a collection of low-dimensional linear subspaces.  ... 
doi:10.1109/ldav.2014.7013202 dblp:conf/ldav/LiuWTBP14 fatcat:j7vcl6uul5eslfopaw3mausyv4

A Study On High Dimensional Clustering By Using Clique

Raghunath Kar, Susanta Kumar Das
2011 International Journal of Computer Science and Informatics  
In real life clustering of high dimensional data is a big problem. To find out the dense regions from increasing dimensions is one of them.  ...  In dimension growth subspace clustering the clustering process start at single dimensional subspaces and grows upward to higher dimensional ones.  ...  high subspaces instead of single dimensional su is 2.  ... 
doi:10.47893/ijcsi.2011.1019 fatcat:pm4ijk6uonadjaz6wdwb2ul6em

DUSC: Dimensionality Unbiased Subspace Clustering

Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
2007 Seventh IEEE International Conference on Data Mining (ICDM 2007)  
In scenarios with many attributes or with noise, clusters are often hidden in subspaces of the data and do not show up in the full dimensional space.  ...  To gain insight into today's large data resources, data mining provides automatic aggregation techniques.  ...  Acknowledgments: This research was funded in part by the cluster of excellence on Ultra-high speed Mobile Information and Communication (UMIC) of the DFG (German Research Foundation grant EXC 89).  ... 
doi:10.1109/icdm.2007.49 dblp:conf/icdm/AssentKMS07 fatcat:xpcpscejrjbpzazkrgannqyl6q

Coordinating Computational and Visual Approaches for Interactive Feature Selection and Multivariate Clustering

Diansheng Guo
2003 Information Visualization  
Specifically, it includes: (1) an interactive feature selection method for identifying potentially interesting, multidimensional subspaces from a high-dimensional data space, (2) an interactive, hierarchical  ...  This paper describes a human-centered exploration environment, which incorporates a coordinated suite of computational and visualization methods to explore high-dimensional data for uncovering patterns  ...  of high-dimensional data.  ... 
doi:10.1057/palgrave.ivs.9500053 fatcat:t6bbfvn5frdhljsilgvnd6y66a

H-D and Subspace Clustering of Paradoxical High Dimensional Clinical Datasets with Dimension Reduction Techniques – a Model

S. Rajeswari, M. S. Josephine, V. Jeyabalaraja
2016 Indian Journal of Science and Technology  
Objectives: Heterogeneous High dimensional data clustering is the analysis of data with multiple dimensions. Large dimensions are not easy to handle.  ...  Dimensionality reduction is the conversion of high dimensional data into a considerable representation of reduced dimensionality that corresponds to the essential dimensionality of the data.  ...  Due to its high complexity in computations of clusters in high dimensional data and with poor cluster accuracy.  ... 
doi:10.17485/ijst/2016/v9i38/101792 fatcat:m5nnpqdfbrcx3nd4rx37ngrkhu

SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces

Josua Krause, Aritra Dasgupta, Jean-Daniel Fekete, Enrico Bertini
2016 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)  
Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature.  ...  First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies  ...  The research described in this paper is part of the Analysis in Motion Initiative at Pacific Northwest National Laboratory (PNNL).  ... 
doi:10.1109/ldav.2016.7874305 dblp:conf/ldav/KrauseDFB16 fatcat:cn3ej6zyhrbm7hrpyvq2uslw6a


2018 Journal of Engineering Science and Technology  
This paper presents a methodology for automatically identifying such patterns to predict a given faulty condition applying the state-of-art techniques of subspace clustering.  ...  The authors propose to summarize an enormously large number of patterns produced by conventional subspace clustering using Similarity connectedness-based Clustering on subspace Clusters (SCoC).  ...  Acknowledgement The authors would like to thank APTRANSO, India for providing the experimental data. SUBCLU PCoC  ... 
doaj:f6e43e1c9a1748a4aba91ac542fe9914 fatcat:jhna245duzhbbbmkpunpactuga

Towards Unsupervised and Consistent High Dimensional Data Clustering

R. G.Mehta, N. J. Mistry, M. Raghuwanshi
2014 International Journal of Computer Applications  
General Terms High dimensional data clustering Keywords High dimensional clustering, Unsupervised and consistent clustering, PROCLUS  ...  High dimensional and large size data results in declined performance of existing clustering algorithms.  ...  PROCLUS is one of the very efficient; subspace clustering from the well of high dimensional clustering algorithms [1] .  ... 
doi:10.5120/15183-3532 fatcat:wuoo3h5phzbmfiisy6vvkeiq7i
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