High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm

Karimov, Jeyhun; Özbayoğlu, A. Murat

doi:10.1109/bigdata.2015.7363909

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…While [18] uses a hybrid model of k-means and GMM, [19] employs a hybrid model based on two evolutionary algorithms. It uses the fireworks-based and cuckoo-search-based evolutionary algorithms to improve the quality of the resulting clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It uses the fireworks-based and cuckoo-search-based evolutionary algorithms to improve the quality of the resulting clusters. In addition to these two algorithms, the method in [19] selects representatives of data using instance reduction to solve the empty cluster issue. The empty clusters problem happens when the number of clusters increases [20].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Alzakan,

Almousa,

Almarzoqi

et al. 2024

IJACSA

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As the volume and complexity of data continue to grow exponentially, finding efficient and accurate clustering algorithms has become crucial for many applications. Kmeans clustering is a widely used unsupervised machine learning technique for data analysis and pattern recognition. Despite its popularity, k-means suffers from certain limitations, such as sensitivity to initial conditions, difficulty in determining the optimal number of clusters, and the potential for misclassification. This research paper proposes an enhanced approach for improving the accuracy and performance of the k-means clustering algorithm by incorporating post-processing techniques using a gradient boosting algorithm. The proposed method comprises training the gradient boosting model on the labeled training set, i.e., the samples with correct cluster assignments obtained from the kmeans algorithm, to predict the correct cluster assignments for the misclassified samples in the testing set. This results in refined cluster assignments for the testing set. The k-means algorithm is only used initially to cluster the data and obtain initial cluster assignments. The effectiveness of the proposed approach is validated through experiments on several benchmark datasets, and the results show a significant improvement in clustering accuracy and robustness compared to the standard k-means algorithm. The proposed approach has the potential to enhance the performance of k-means in various real-world applications and domains.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Alzakan,

Almousa,

Almarzoqi

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…This nature-inspired metaheuristic approach simulates the explosion of fireworks to enhance the search process within complex solution spaces. Through its utilization, the algorithm has exhibited remarkable efficacy in solving a variety of optimization problems, ranging from engineering design and parameter tuning to financial portfolio optimization ( Karimov & Ozbayoglu, 2015 ; Rahmani et al, 2015 ).…”

Section: Related Workmentioning

confidence: 99%

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

Li,

2023

PeerJ Computer Science

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The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual’s psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students’ mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means—its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students’ mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students’ mental well-being during times of fluctuating public health dynamics.

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“…This achieves higher accuracy than conventional kmeans. Another enhanced version of k-means clustering was suggested in [116], to help eliminate the empty clustering problem of traditional k-means. The clustering approach was based on a combination of Fireworks and Cuckoo-search algorithms with representative points being selected as the centroids.…”

Section: Big Data Clusteringmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm

Cited by 14 publications

References 16 publications

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

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