EvolveCluster: an evolutionary clustering algorithm for streaming data

Nordahl, Christian; Boeva, Veselka; Grahn, Håkan; Netz, Marie Persson

doi:10.1007/s12530-021-09408-y

Cited by 10 publications

(8 citation statements)

References 49 publications

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“…EvolveCluster [1] is a sequential data stream clustering algorithm capable of modeling consistent and changing behaviors in a data stream. It builds upon dividing a stream into specified segments, or windows, and clusters them sequentially as they arrive.…”

Section: Ms-ec Modulesmentioning

confidence: 99%

“…This paper proposes a novel distributed multi-stream (multi-view) clustering algorithm entitled MultiStream EvolveCluster (MS-EC). A multi-source extension of our previously introduced algorithm EvolveCluster [1]. We design and conduct several experiments to investigate how the MS-EC algorithms perform, using synthetic and real-world data, multiple consensus clustering algorithms, and against a baseline algorithm.…”

Section: Introductionmentioning

confidence: 99%

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MultiStream EvolveCluster

Nordahl

Boeva

Grahn

2023

Proceedings of the Canadian Conference on Artificial Intelligence

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This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis of evolving time series phenomena. It can maintain evolving clustering solutions separately for each stream/view and consensus clustering solutions reflecting evolving interrelations among the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. The properties of the MultiStream EvolveCluster algorithm are studied and evaluated with respect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation results show a stable algorithm performance in synthetic data scenarios. In the case of real-world data, the algorithm behavior demonstrates sensitivity to the individual streams' data quality and the used consensus clustering technique.

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Section: Ms-ec Modulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MultiStream EvolveCluster

Nordahl

Boeva

Grahn

2023

Proceedings of the Canadian Conference on Artificial Intelligence

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“…There exist several data stream clustering algorithms in the literature [12][13][14][15][16][17]. Most of the data stream clustering algorithms use a two-phase approach [16].…”

Section: Related Workmentioning

confidence: 99%

“…Cluster Mapping Measure [42] is another data stream clustering validation metric. However, all the aforementioned metrics are for clustering algorithms that process streaming instances individually [15]. On the contrary, EmCStream processes the data stream window by window.…”

Section: Metricsmentioning

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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“…1) dynamic unsupervised and semi-supervised learning models that are robust to the appearance of drifting context and additionally enable to learn from multiple data sources by distributed training, and continual updating and evolving of the model [4], [6], [7], [8]; 2) development of dynamic techniques for automatic annotation (labeling) of the data; 3) usage of transfer learning techniques enabling reuse of knowledge from training in earlier tasks to subsequent tasks. The other research ambition of DAISeN is the design of distributed/composable data mining models.…”

Section: Research Objectivesmentioning

confidence: 99%

Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)

Boeva¹,

Casalicchio²,

Abghari³

et al. 2022

Annals of Computer Science and Information Systems

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This position paper describes the aims and preliminary results of the Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN) 1 project. The project ambition is to address today's edge AI challenges by developing advanced AI techniques that model knowledge from the sensor network and the environment to support the deployment of sustainable AI applications. We present one of the use cases being considered in DAISeN and review the state-of-the-art in three research domains related to the use case presented and directly falling into the project scope. We additionally outline the main challenges identified in each domain. The developed Global Navigation Satellite Systems (GNSS) activation model addressing the use case challenges is also briefly introduced. The future research studies planned for the remaining period of the project are finally outlined. I. INTRODUCTIONT HE NUMBER of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems has been growing recently. These solutions facilitate the creation of new smart products and services in many different fields. In addition, sensor networks are undergoing great expansion and development and the integration of AI and sensor networks benefits many areas such as Industry 4.0, healthcare, mobility, logistics, and many other Internetof-Things (IoT) applications. However, this has also put new challenges in front of researchers and practitioners. New realtime AI and ML algorithms are needed along with different strategies to embed these algorithms in sensor boards and network nodes such as fog/edge nodes. For example, edgebased AI requires robust and adaptive models that take into account the temporal component of a data flow and allow for vertical and horizontal scaling of the decision-making process. These models must employ efficient learning algorithms that are capable of dealing with information varying over time and coping with large scale missing and inaccurate values. In addition, the decision-making models should be composable so that they can be distributed on the edge devices in order to This work is part of the Sony RAP 2020 Project "Distributed and Adaptive Edge-based AI Models for Sensor Networks"1 Daisen is a volcanic mountain located in Tottori Prefecture, Sanin Region of Japan. ensure a trade-off between the decision accuracy, latency, and consumed energy per decision.The IoT is an emerging key technology for future industries and the everyday lives of people, e.g., it has been playing an increasingly important role in healthcare, agriculture, home services, industrial processes, and transportation. Wireless Sensor Network (WSN) is an enabling technology for IoT [1], and, by definition, is the bridge between the physical world and the intelligence residing on the Internet. The integration of AI and sensor networks (by means of the IoT) are now realities that are changing our lives. Sensor networks are widely used to collect environmental parameters, e.g., in homes, buildings, and vehicles, where they a...

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EvolveCluster: an evolutionary clustering algorithm for streaming data

Cited by 10 publications

References 49 publications

MultiStream EvolveCluster

MultiStream EvolveCluster

Online embedding and clustering of evolving data streams

Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)

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