Online reliable semi-supervised learning on evolving data streams

Din, Salah Ud; Shao, Jun; Kumar, Jay; Ali, Waqar; Liu, Jiaming; Ye, Yu

doi:10.1016/j.ins.2020.03.052

Cited by 51 publications

(9 citation statements)

References 27 publications

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“…Another example is the optimization of routing in the MANET (Mobile Ad-hoc Network) and, consequently, reducing energy consumption and increasing the network's useful life and stability using ICA (Imperialist Competitive Algorithm) [29]. Finally, in [30], a semi-supervised algorithm was used to classify data from eleven real-world datasets, some of which are electricity, chemical sensors, e-mails, and TCP type limit records.…”

Section: Background and Related Workmentioning

confidence: 99%

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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Section: Background and Related Workmentioning

confidence: 99%

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

2022

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“…This dataset also contains abrupt concept drift simulated by four different concepts every 12,500 data points by changing the class decision boundary. − CR4 [20] is an artificial dataset containing 144,400 samples using four classes rotating separately in 2dimensional space. − FG2C2D [20] is an artificial dataset that contains two bidimensional classes and two concept drift, namely gradual and incremental concept drift, every 200 data points.…”

Section: Datasetsmentioning

confidence: 99%

“…− CR4 [20] is an artificial dataset containing 144,400 samples using four classes rotating separately in 2dimensional space. − FG2C2D [20] is an artificial dataset that contains two bidimensional classes and two concept drift, namely gradual and incremental concept drift, every 200 data points. There are 200,000 samples and two classes in this dataset.…”

Section: Datasetsmentioning

confidence: 99%

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Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

Sihombing

Zakir

2021

Bulletin EEI

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When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.

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“…Although, ant colony optimization methods are also being used by a number of data stream clustering algorithms (Fahy et al, 2018), they are not evaluated at this time. Moreover, being an active research area, there also exist several recent data stream clustering algorithms that are not evaluated in this manuscript (Bezerra et al, 2020;Din et al, 2020;Kim and Park, 2020).…”

Section: Stream Clustering Algorithmsmentioning

confidence: 99%

Data stream clustering: a review

Zubaroğlu

Atalay

2020

Artif Intell Rev

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Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for real-time data stream processing, because it can be applied with less prior information about the data and it does not need labeled instances. However, data stream clustering differs from traditional clustering in many aspects and it has several challenging issues. Here, we provide information regarding the concepts and common characteristics of data streams, such as concept drift, data structures for data streams, time window models and outlier detection. We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy. A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms. Open problems about data stream clustering are also discussed. Keywords Data streams • Data stream clustering • Real-time clustering • 1 Introduction More devices including sensors are becoming interconnected and interconnected devices continuously generate streams of data at high speed. Offline processing of

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Online reliable semi-supervised learning on evolving data streams

Cited by 51 publications

References 27 publications

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

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

Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

Data stream clustering: a review

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