Synchronization-based clustering on evolving data stream

Shao, Jun; Tan, Yue; Gao, Lianli; Yang, Qinli; Plant, Claudia; Assent, Ira

doi:10.1016/j.ins.2018.09.035

Cited by 32 publications

(13 citation statements)

References 24 publications

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“…However, STRAP [7], one of the latest methods of partition-based DSC algorithms, has been successfully employed to process the KDD'99 dataset (34D). Compared to partition-based DSC methods, density-based [6], [9] and synchronization-based ones [29] are more suitable for processing high-dimensional data streams because, in theory, they can find arbitrary-shaped clusters that exist in the entire feature space without requiring the number of clusters. For example, DenStream [6], CEDAS (density based) [9], and SyncTree (synchronization based) [29] are recently proposed and hold state-of-the-art performance among full-space DSC algorithms.…”

Section: A High-dimensional Data Stream Clusteringmentioning

confidence: 99%

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Sui

Liu

et al. 2022

IEEE Trans. Cybern.

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In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

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Section: A High-dimensional Data Stream Clusteringmentioning

confidence: 99%

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Sui

Liu

et al. 2022

IEEE Trans. Cybern.

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“…In the realm of IoT, data streams are common in many applications, such as for comprehensive web searching, the real-time detection of anomalies within network traffic, social networks, environmental monitoring, cyberphysical systems and sensor networks. In these applications, data evolve significantly over time and continuously arrive [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many researchers have proposed density-based data stream clustering algorithms. However, several issues related to these clustering algorithms must be considered [13,[52][53][54][55][56][57][58][59], such as most are not entirely online methods, are unable to handle evolving data streams, are unable to manage the noisy characteristics of data streams, or suffer from high memory requirements, low processing rates, or the "curse of dimensionality" [45,[60][61][62][63]. Moreover, the existing density-based clustering algorithms have high computational times and low cluster quality for clustering data streams.…”

Section: Introductionmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The gridbased method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality. INDEX TERMS Clustering, data stream, evolving, grid-based method, core-micro-cluster, online.

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“…Systems in the real world can be abstracted into complex networks, and a large number of algorithms for complex network mining have been proposed [1][2][3][4][5], most of which focus on static networks. However, most networks in the real world are evolving over time.…”

Section: Introductionmentioning

confidence: 99%

Identifying Communities in Dynamic Networks Using Information Dynamics

Sun

Sheng

Wang

et al. 2020

Entropy

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Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

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Synchronization-based clustering on evolving data stream

Cited by 32 publications

References 24 publications

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Identifying Communities in Dynamic Networks Using Information Dynamics

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