Clustering stream data by exploring the evolution of density mountain

Gong, Shufeng; Zhang, Yanfeng; Yu, Ge

doi:10.1145/3186728.3164136

Cited by 36 publications

(37 citation statements)

References 28 publications

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“…Even though the self-expressiveness property has been successfully used to perform SC on static datasets, it is quite challenging to achieve dynamic SC on data streams based on the property because it is hard to balance two conflicting goals: 1) saving points for good SC performance and 2) discarding points for low computational complexity. In this section, an efficient algorithm, EDSSC, is proposed for balancing the two competing goals and addressing (8), that is, performing DSC on evolving data streams.…”

Section: Proposed Edssc Approachmentioning

confidence: 99%

“…Most existing DSC algorithms, including the classic ones, such as CluStream [5] and DenStream [6], or even the more recent ones, such as STRAP [7], EDMStream [8], and CEDAS [9], are inadequate to address these challenges. In addition, most existing DSC algorithms are based on nonevolutionary models (e.g., CluStream) or simple evolutionary models (e.g., DenStream, STRAP, and CEDAS) which cannot adapt to the complicated dynamics of clusters' structures in the real world [8], [10]. Therefore, there is a pressing need to study more effective DSC algorithms to process evolving data streams that are high dimensional and large scale.…”

Section: Introductionmentioning

confidence: 99%

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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: Proposed Edssc Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Sui

Liu

et al. 2022

IEEE Trans. Cybern.

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“…However, when updating the center point of the traveling buddy, the accumulation of the offset increases the sensitivity of outliers of the algorithm. Gong et al [Gong, Zhang and Yu (2017)] used a cluster-cell data structure in data stream clustering to represent a set of close points. However their methods cannot apply to discover the movement pattern directly due to the time series property of spatio-temporal data.…”

Section: Clustering Moving Objectsmentioning

confidence: 99%

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

Zhang¹,

Ji²,

Zhao³

et al. 2019

Computers, Materials &Amp; Continua

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The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

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“…Clustering optimization cab be used to find similar entities in the same evolution stages [Gong, Zhang and Yu (2017); Hyde, Angelov and MacKenzie (2017); Puschmann, Barnaghi and Tafazolli (2017); Bodyanskiy, Tyshchenko and Kopaliani (2017)]. There are some latest work on data stream clustering in entity community detection [de Andrade Silva, Hruschka and Gama (2017); Hyde, Angelov and MacKenzie (2017); Yarlagadda, Jonnalagedda and Munaga (2018)].…”

Section: Clustering Optimizationmentioning

confidence: 99%

Stream-Based Data Sampling Mechanism for Process Object

Lin¹,

Liu²,

Chen³

et al. 2019

Computers, Materials &Amp; Continua

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Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. R ecently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. T here i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.

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Clustering stream data by exploring the evolution of density mountain

Cited by 36 publications

References 28 publications

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

Stream-Based Data Sampling Mechanism for Process Object

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