An effective density-based clustering and dynamic maintenance framework for evolving medical data streams

Al-Shammari, Ahmed; Zhou, Rui; Naseriparsa, Mehdi; Liu, Chengfei

doi:10.1016/j.ijmedinf.2019.03.016

Cited by 31 publications

(16 citation statements)

References 25 publications

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“…In most real-life scenarios streams are not predefined as balanced or imbalanced and they may become imbalanced only temporarily (Wang et al, 2018). Users' interests over time (where new topics emerge and old ones lose relevance) (Wang et al, 2014), social media analysis (Liu et al, 2020), or medical data streams (Al-Shammari et al, 2019) are examples of such cases. Therefore, a robust data stream mining algorithm should display high predictive performance regardless of the underlying class distributions (Fernández et al, 2018).…”

Section: Imbalanced Data Streamsmentioning

confidence: 99%

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Aguiar¹,

Krawczyk²,

Cano³

2022

Preprint

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Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.

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Section: Imbalanced Data Streamsmentioning

confidence: 99%

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Aguiar¹,

Krawczyk²,

Cano³

2022

Preprint

View full text Add to dashboard Cite

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“…But, this optimization method is not used with multiobjective optimization functions for initializing the clustering centroid to process high-frequency data streams. en, in 2019, Al-Shammari et al [17] proposed density-based clustering in the sense of dynamic framework for classifying the medical data with the help of piece-wise aggregate approximation and the densitybased spatial clustering algorithm to enhance the better performance and maintenance of the dynamic cluster. But, this proposed algorithm is not considered about high frequency of incoming data streams for updating and maintaining the data clusters.…”

Section: E Major Contribution Of the Research Papermentioning

confidence: 99%

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Rani

Pravin

2022

Advances in Fuzzy Systems

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Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the current input medical data, and then in the second phase, important features are chosen with the help of the Tversky index with holoentropy. Finally, medical data clustering has been carried out by using hybrid K-means-based rider sunflower optimization (RSFO) algorithm. RSFO is designed to produce optimum clustering centroid, which is the combination of two optimization techniques, such as rider optimization algorithm (ROA) and sunflower optimization (SFO). This hybrid algorithm can get the advantages of both K-means and RSFO technique and avoid premature convergence of K-means algorithm and high computation cost of optimization technique. K-Means clustering algorithm is used to cluster the medical data by using an optimum centroid. The efficiency of the proposed K-means-based rider sunflower optimization algorithm is examined with a heart disease data set and analyzed based on three different performance metrics.

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“…Initially, the concatenation of features is performed using different modalities and heterogeneous data sets, they are unable to generate the required results. Secondly, these methods pose high time complexity issues, which are unable to cluster huge amounts of heterogeneous data 47 . The real‐life data may contain unidentifiable clusters and the order, wherein the tuples may affect the results when the algorithm is implemented with faulty distance measures.…”

Section: Motivationmentioning

confidence: 99%

Rainfall flow optimization based K‐Means clustering for medical data

Rani

Pravin

2021

Concurrency and Computation

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SummaryIn the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set.

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An effective density-based clustering and dynamic maintenance framework for evolving medical data streams

Cited by 31 publications

References 25 publications

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Rainfall flow optimization based K‐Means clustering for medical data

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