DBIECM-an evolving clustering method for streaming data clustering

Zhang, Luo; Lan, Tian; Zhang, Xuanya; Li, Lin

doi:10.18280/ama_b.600115

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…DPClust (Xu et al [35]), CEDAS (Hyde et al [23]), DBIECM (Zhang et al [37]), FEAC-Stream (Andrade Silva et al [15]) and Adaptive Stream k-means (Puschmann et al [30]) are the fully online clustering algorithms.…”

Section: Fully Online Clusteringmentioning

confidence: 99%

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CPOCEDS-concept preserving online clustering for evolving data streams

Jafseer,

Shailesh,

Sreekumar

2023

Cluster Comput

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Clustering streaming data is challenging due to many temporal dynamics, such as concept drift, concept evolution, and feature evolution. Concept evolution is the most challenging of these. Due to concept evolution, new classes may emerge or existing classes may disappear, so it is crucial to process streaming data continuously. This paper proposes a novel online clustering method, specifically for streaming data with concept evolution. It consists of three phases: initialization, clustering and outlier handling. To identify recurrences of previous data in streaming data, it is critical to preserve the sequential properties of data chunks. In the proposed model, representatives from previous windows are added to the current window, making it distinct from existing models. The detection and handling of outliers are very challenging tasks in streaming data analysis. Outliers are often the first instances of a new cluster. The proposed model stores the outliers from each data window. When the number of outliers exceeds a certain threshold, the representatives of outliers are added to the next window to identify new classes. To handle the lack of data sets for the training of such models, we created a synthetic data set with 22020 data instances. Using Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index analysis, this model yielded the most favourable results.

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Section: Fully Online Clusteringmentioning

confidence: 99%

“…DBIECM (Zhang et al [37]) is an online, distance-based, evolving data stream clustering technique that utilizes the Davies Bouldin Index (DBI) rather than the shortest distance as the assessment criterion. It is a better variant of the Evolving Clustering Method (ECM) (Song et al [24]).…”

Section: Fully Online Clusteringmentioning

confidence: 99%

CPOCEDS-concept preserving online clustering for evolving data streams

Jafseer,

Shailesh,

Sreekumar

2023

Cluster Comput

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“…A number of data stream clustering algorithms that are not described in recent surveys are adaptive streaming k ‐means [24], fast evolutionary algorithm for clustering data streams (FEAC‐Stream) [25], multi density data stream clustering algorithm (MuDi‐Stream) [26], clustering of evolving data streams into arbitrarily shaped clusters (CEDAS) [27], improved data stream clustering algorithm [28], davies‐bouldin index evolving clustering method for streaming data clustering (DBIECM) [29] and improved hierarchical density‐based clustering (I‐HASTREAM) [30, 31]. These most recent algorithms are reviewed comprehensively by Zubaroğlu and Atalay [32].…”

Section: Related Workmentioning

confidence: 99%

Online embedding and clustering of evolving data streams

Zubaroğlu

Atalay

2022

Statistical Analysis

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Number of connected devices is steadily increasing and this trend is expected to continue in the near future. Connected devices continuously generate data streams and the data streams may often be high dimensional and contain concept drift. Clustering is one of the most suitable methods for real-time data stream processing, since clustering can be applied with less prior information about the data. Also, data embedding makes the visualization of high dimensional data possible and may simplify clustering process. There exist several data stream clustering algorithms in the literature; however, no data stream embedding method exists. Uniform Manifold Approximation and Projection (UMAP) is a data embedding algorithm that is suitable to be applied on stationary (stable) data streams, though it cannot adapt concept drift. In this study, we describe a novel method EmCStream, to apply UMAP on evolving (nonstationary) data streams, to detect and adapt concept drift and to cluster embedded data instances using a distance or partitioning-based clustering algorithm. We have evaluated EmCStream against the state-of-the-art stream clustering algorithms using both synthetic and real data streams containing concept drift. EmCStream outperforms DenStream and CluStream, in terms of clustering quality, on both synthetic and real evolving data streams. Datasets

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“…This algorithm has improved the updated process of the cluster centre and radius on (ECM) evolutionary clustering method and takes Davies-Bouldin Index (DBI) as classification criterion. A round refers to the successive time interval between the two cluster heads [11][12][13][14][15][16][17][18][19][20][21]. Message that contains location information are being shared among the nodes in the field and it does not require flooding and complicated computation for localization [22].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Energy Balanced Fault Tolerant Multipath Routing Protocol (HEBFTMRP) for Wireless Sensor Networks

Kumaran¹,

Nagarajan²

2019

AMA_D

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In this paper, the performance of hierarchical routing and fault tolerant routing scheme are combined and proposed a protocol named Hierarchical Energy Balanced Fault Tolerant Multipath Routing (HEBFTMR). Due to the hierarchical scheme adopted, cluster head energy level is saved. Cluster head in lower region can communicate to the higher region cluster head via the intermediate level cluster heads. Cluster head is changed frequently to equalize the load among the cluster members in the cluster. This cluster head is selected based on the following factors: residual energy, node weightage, distance between the neighbour nodes and the distance between the neighbour node and base station. Multipath routing is established to transmit data from the member node to the cluster head node for efficient data transmission. This is necessary due to the nature of sensors has frequent link broken. If the link is broken, neighbour node selection is important to route the data efficiently. The tolerant energy level of the sensor for route the data is determined. When the energy level decreases below the threshold value, other neighbour node is selected. Simulation results reveal that the proposed scheme proves that this combined scheme performs well when compared to the existing scheme in WSN environment.

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DBIECM-an evolving clustering method for streaming data clustering

Cited by 5 publications

References 20 publications

CPOCEDS-concept preserving online clustering for evolving data streams

CPOCEDS-concept preserving online clustering for evolving data streams

Online embedding and clustering of evolving data streams

Hierarchical Energy Balanced Fault Tolerant Multipath Routing Protocol (HEBFTMRP) for Wireless Sensor Networks

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