A dynamic hierarchical incremental learning-based supervised clustering for data stream with considering concept drift

Nikpour, Soheila; Asadi, Shahrokh

doi:10.1007/s12652-021-03673-0

Cited by 9 publications

(3 citation statements)

References 70 publications

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“…The proposed technique can address the concept-evolving nature of the data streams by adapting the clustering structure with the help of merge and split operations. Nikpour et al proposed a new incremental learning-based method, called DCDSCD (Dynamic Clustering of the Data Stream with considering Concept Drift) [10], which is an incremental supervised clustering algorithm. In the proposed algorithm, the data stream is automatically clustered in a supervised manner, where the clusters whose values decrease over time are identified and then eliminated.…”

Section: Related Workmentioning

confidence: 99%

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Xu,

Lü,

Zhang

et al. 2024

CMC

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Data stream clustering is integral to contemporary big data applications. However, addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research. This paper aims to elevate the efficiency and precision of data stream clustering, leveraging the TEDA (Typicality and Eccentricity Data Analysis) algorithm as a foundation, we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm. The original TEDA algorithm, grounded in the concept of "Typicality and Eccentricity Data Analytics", represents an evolving and recursive method that requires no prior knowledge. While the algorithm autonomously creates and merges clusters as new data arrives, its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data. This work presents the NS-TEDA (Neighbor Search Based Typicality and Eccentricity Data Analysis) algorithm by incorporating a KD-Tree (K-Dimensional Tree) algorithm integrated with the Scapegoat Tree. Upon arrival, this ensures that new data points interact solely with clusters in very close proximity. This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent. We apply the NS-TEDA algorithm to several well-known datasets, comparing its performance with other data stream clustering algorithms and the original TEDA algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy, and its runtime exhibits almost linear dependence on the volume of data, making it more suitable for large-scale data stream analysis research.

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Section: Related Workmentioning

confidence: 99%

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Xu,

Lü,

Zhang

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…In (Nikpour and Asadi, 2022), developed dynamic clustering of data flow with the consideration of CD i.e., an incremental supervised clustering model. In the presented method, data flow is clustered automatically in a supervised way, where the cluster value decreases over time are recognized and then removed.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream Classification

Priya¹,

Uthra²

2022

Journal of Computer Science

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Concept drift and class imbalanced data are major challenging processes involved in modern streaming data classification. Particularly, when integrated with difficult factors like the existence of noise, overlapping class distribution, concept drift, and data imbalance can considerably affect the classifier results. In addition, various challenges affect the performance of the existing oversampling schemes such as SMOTE and its derivatives. Regardless of that, several existing models concentrate on the data imbalance in the binary classification problems, whereas the complex multi-class counterparts are yet to be explored. With this motivation, this study develops an Adaptive Synthetic Oversampling Algorithm (ASYNO) based Multiclass Streaming Data Classification (ASYNO-MCSDC) model on Class Imbalance Handling and Concept Drift. The presented ASYNO-MCSDC method initially performs different stages of preprocessing such as label encoding, data normalization, and data splitting. Besides, the Adaptive Synthetic oversampling technique (ASYNO) is applied for handling class imbalance data problems. Also, the online bagging ensemble classifier is employed for the data classification process in which the Hoeffding Tree (HT) was utilized as the base classification and the number of estimators used in online bagging is set to 10. For the process of experimentation, two types of learning are used, one is batch learning and other is incremental learning. The experimental validation of the ASYNO-MCSDC model is tested using two datasets namely stationary imbalance stream and dynamic imbalance stream. The experimental results pointed out that the ASYNO-MCSDC model has accomplished promising results over other models.

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“…HUE-Stream [26] heterojen belirsizliğe sahip çeşitli akan veriler için E-Stream'in bir uzantısı olarak sunulan hiyerarşik bir algoritmadır. Nikpour ve Asadi [27], hiyerarşik bir yaklaşımla akan verileri kümelemek için kavram kayması dikkate alınarak akan verinin dinamik kümelenmesi yöntemini önermişlerdir. Sangma vd.…”

Section: Giriş (Introduction)unclassified

Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması

Şenol

Kaya

Canbay

2023

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Teknolojideki gelişmeler, insanların pek çok farklı kaynakta üretilen verileri toplamasına ve analiz etmesine imkân tanımıştır. Sensörler, mobil cihazlar, nesnelerin interneti gibi yapılarda üretilen veriler akan veri formatında olup, bu tür verilerden işlenerek faydalı bilgilerin elde edilmesi zor bir problemdir. Akan verileri analiz etmek için sıklıkla kullanılan yöntemlerden birisi olan kümelemede, veriler dağılımlarına göre çeşitli gruplara ayrılarak analiz edilir. Bu çalışmada, akan veri kümeleme problemi için iki yeni algoritma geliştirilerek literatürdeki başka bir yöntemle karşılaştırılmıştır. Farklı veri kümeleri üzerinde yapılan deneyler neticesinde, geliştirilen algoritmaların iyi sonuçlar verdiği görülmüştür.

show abstract

A dynamic hierarchical incremental learning-based supervised clustering for data stream with considering concept drift

Cited by 9 publications

References 70 publications

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Adaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream Classification

Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması

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