Nonlinear skeletons of data sets and applications—Methods based on subspace clustering

2008 ◽  
pp. 95-108
Author(s):  
Pando Georgiev
Keyword(s):  
Subspace Clustering ◽  
Data Sets

scholarly journals Large-Scale Multi-View Subspace Clustering in Linear Time

2020 ◽  
Vol 34 (04) ◽  
pp. 4412-4419 ◽  
Author(s):  
Zhao Kang ◽  
Wangtao Zhou ◽  
Zhitong Zhao ◽  
Junming Shao ◽  
Meng Han ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Linear Time ◽  
Subspace Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Single View ◽  
Novel Approach ◽  
Points Of View ◽  
Effectiveness And Efficiency

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.

A Novel Scalable Signature Based Subspace Clustering Approach for Big Data

2019 ◽  
Vol 14 (2) ◽  
pp. 41-51 ◽  
Author(s):  
T. Gayathri ◽  
D. Lalitha Bhaskari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Subspace Clustering ◽  
Distance Measures ◽  
Data Sets ◽  
Management Tools ◽  
Clustering Approach ◽  
Different Dimensions

“Big data” as the name suggests is a collection of large and complicated data sets which are usually hard to process with on-hand data management tools or other conventional processing applications. A scalable signature based subspace clustering approach is presented in this article that would avoid identification of redundant clusters. Various distance measures are utilized to perform experiments that validate the performance of the proposed algorithm. Also, for the same purpose of validation, the synthetic data sets that are chosen have different dimensions, and their size will be distributed when opened with Weka. The F1 quality measure and the runtime of these synthetic data sets are computed. The performance of the proposed algorithm is compared with other existing clustering algorithms such as CLIQUE.INSCY and SUNCLU.

scholarly journals Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

2019 ◽  
Vol 11 (12) ◽  
pp. 254
Author(s):  
Zihe Zhou ◽  
Bo Tian
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Online Social Network ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Data Sets ◽  
Text Data ◽  
New Community

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.

scholarly journals A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 906
Author(s):  
Muhammad Azhar ◽  
Mark Junjie Li ◽  
Joshua Zhexue Huang
Keyword(s):  
Mixture Model ◽  
State Of The Art ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Data Sets ◽  
Ensemble Clustering ◽  
Class Label ◽  
Number Of Clusters ◽  
Number Of Classes

Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

Incomplete high dimensional data streams clustering

2020 ◽  
Vol 39 (3) ◽  
pp. 4227-4243
Author(s):  
Fatma M. Najib ◽  
Rasha M. Ismail ◽  
Nagwa L. Badr ◽  
Tarek F. Gharib
Keyword(s):  
Data Streams ◽  
Missing Values ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
Streaming Data ◽  
High Dimensionality ◽  
High Dimensional ◽  
Data Sets ◽  
Multiple Data ◽  
Sensitivity Specificity

Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.

scholarly journals Subspace Clustering of High-Dimensional Data: An Evolutionary Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Singh Vijendra ◽  
Sahoo Laxman
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Data Points

Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. In this paper, we have presented a robust multi objective subspace clustering (MOSCL) algorithm for the challenging problem of high-dimensional clustering. The first phase of MOSCL performs subspace relevance analysis by detecting dense and sparse regions with their locations in data set. After detection of dense regions it eliminates outliers. MOSCL discovers subspaces in dense regions of data set and produces subspace clusters. In thorough experiments on synthetic and real-world data sets, we demonstrate that MOSCL for subspace clustering is superior to PROCLUS clustering algorithm. Additionally we investigate the effects of first phase for detecting dense regions on the results of subspace clustering. Our results indicate that removing outliers improves the accuracy of subspace clustering. The clustering results are validated by clustering error (CE) distance on various data sets. MOSCL can discover the clusters in all subspaces with high quality, and the efficiency of MOSCL outperforms PROCLUS.

scholarly journals Projection subspace clustering

2017 ◽  
Vol 11 (3) ◽  
pp. 224-233
Author(s):  
Xiaoyun Chen ◽  
Mengzhen Liao ◽  
Xianbao Ye
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
High Dimension ◽  
Dimensional Space ◽  
Subspace Clustering ◽  
Low Rank ◽  
High Dimensional ◽  
Data Sets ◽  
Expression Data ◽  
Low Rank Representation

Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension. Because some subspace segmentation approaches can be better applied in the high-dimensional space, three new subspace clustering models for gene expression data sets are proposed in this work. The proposed projection subspace clustering models have projection sparse subspace clustering, projection low-rank representation subspace clustering and projection least-squares regression subspace clustering which combine projection technique with sparse subspace clustering, low-rank representation and least-square regression, respectively. In order to compute the inner product in the high-dimensional space, the kernel function is used to the projection subspace clustering models. The experimental results on six gene expression data sets show these models are effective.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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