BEstream: Batch Capturing with Elliptic Function for One-Pass Data Stream Clustering

Wattanakitrungroj, Niwan; Maneeroj, Saranya; Lursinsap, Chidchanok

doi:10.1016/j.datak.2018.07.002

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…Since it does not use the entire data stream, it suffers the risks of losing important historical information. Two recent one-pass density-based streaming clustering algorithms, namely HCMstream [28] and BEstream [29], are proposed to apply the discard-after-cluster concept to record the entire history of the streaming data without causing the memory overflow. As the first density-based projected clustering method, HDDstream [30] was proposed to address the high-dimensionality of data streams.…”

Section: A State-of-the-art Methods Reviewmentioning

confidence: 99%

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

Yan

Razeghi-Jahromi²,

Homaifar

et al. 2019

IEEE Access

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As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown. Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance. The overlap among clusters is another major challenge for the existing stream clustering methods. These constraints strongly limit their real-time applications. In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed. It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem. It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers. To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm. The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided. Results show comparable or better performance relative to five popular data stream clustering algorithms. A scalability analysis of the proposed streaming clustering method is presented in this paper as well.INDEX TERMS Data streams, clustering, unsupervised learning, data mining. I. INTRODUCTION

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Section: A State-of-the-art Methods Reviewmentioning

confidence: 99%

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

Yan

Razeghi-Jahromi²,

Homaifar

et al. 2019

IEEE Access

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show abstract

“…Many recent and interesting proposals address the data stream clustering problem using the microcluster and macrocluster concepts in literature. Among them, we can cite the HCMstream [19], VHEC [33], BEstream [20], ACSC [34], DyClee [21], and DGStream [22]. The HCMstream, Hyper-cylindrical micro-clustering for streaming data [19], proposes to use hyper-cylindrical forms to merge the spherical microclusters.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The VHEC, Versatile Hyper-Elliptic Clustering [33], and BEstream, Batch Capturing with Elliptic Function for One-Pass Data Stream Clustering [20], create hyper-elliptic microclusters with arbitrary rotation. Both algorithms determine a new similarity distance measure to the data stream clustering, and the BEstream can be used in high-dimensional space.…”

Section: Background and Related Workmentioning

confidence: 99%

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Macro SOStream: An Evolving Algorithm to Self Organizing Density-Based Clustering with Micro and Macroclusters

2022

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This paper proposes a new evolving algorithm named Macro SOStream with entirely online learning and based on self-organizing density for data stream clustering. The Macro SOStream is based on the SOStream algorithm, but we incorporate macroclusters composed of microclusters. While microclusters have spherical shapes, macroclusters can have arbitrary shapes. Moreover, the Macro SOStream has the macrocluster merge functionality specially designed to improve its performance under data drift contexts. The Macro SOStream’s performance is compared to SOStream and DenStream algorithms’ performance using four synthetic datasets and the ARI performance metric to validate our proposal. Furthermore, we carry out an exhaustive analysis on the influence of adequate hyperparameter setup on these algorithms’ performance. As a result, the Macro SOStream presents good performance mainly in the context of data drift and for demands of non-spherical clusters.

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“…The BEStream algorithm [18] takes the micro-cluster concept and extends it by introducing the idea of elliptic micro-clusters. In BEStream clusters are in the form of hyper-dimensional ellipses arranged along the eigenvectors of the data distribution, which differs from the standard hyper-spherical micro-clusters used in other algorithms.…”

Section: Previous Workmentioning

confidence: 99%

Using distribution analysis for parameter selection in repstream

Callister¹,

Pham²,

Lazarescu³

2019

Mathematical Foundations of Computing

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One of the most significant challenges in data clustering is the evolution of the data distributions over time. Many clustering algorithms have been introduced to deal specifically with streaming data, but common amongst them is that they require users to set input parameters. These inform the algorithm about the criteria under which data points may be clustered together. Setting the initial parameters for a clustering algorithm is itself a non-trivial task, but the evolution of the data distribution over time could mean even optimally set parameters could become non-optimal as the stream evolves. In this paper we extend the RepStream algorithm, a combination graph and density-based clustering algorithm, in a way which allows the primary input parameter, the K value, to be automatically adjusted over time. We introduce a feature called the edge distribution score which we compute for data in memory, as well as introducing an incremental method for adjusting the K parameter over time based on this score. We evaluate our methods against RepStream itself, and other contemporary stream clustering algorithms, and show how our method of automatically adjusting the K value over time leads to higher quality clustering output even when the initial parameters are set poorly.

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BEstream: Batch Capturing with Elliptic Function for One-Pass Data Stream Clustering

Cited by 7 publications

References 22 publications

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

Macro SOStream: An Evolving Algorithm to Self Organizing Density-Based Clustering with Micro and Macroclusters

Using distribution analysis for parameter selection in repstream

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