WITHDRAWN: Data leak identification using scattering search K Means in social networks

Karthik, Jayavarapu; Tamizhazhagan, V.; Narayana, S.

doi:10.1016/j.matpr.2021.01.200

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The strong mathematical framework of the SDS algorithm describes its behavior in relation to resource allocation, global optimum convergence, and linear time complexity with robustness and criteria for minimal convergence. Karthik, Tamizhazhagan, and Narayana [185] proposed a stochastic diffusion search K-means clustering technique named 'scattering search K-means' (SS-K means) for locating optimal clustering points for the identification of points of data leakage in social networks.…”

Section: Stochastic Diffusion Searchmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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Section: Stochastic Diffusion Searchmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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“…It is a statistical analysis method that is used to study the classification problem (sample or index) using an essential data mining algorithm. Among the popular clustering algorithms, the K-means is the one that is simple, fast, and center-based [63]. In addition, Ma and Yong [64] also used the K-means algorithm for short text in social networks.…”

Section: Clustering Analysismentioning

confidence: 99%

A Social Network-Based Examination on Bid Riggers’ Relationships in the Construction Industry: A Case Study of China

Xiao

Zhou

et al. 2021

Buildings

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Collusive bidding has been an insidious issue in the construction industry. Bidders initiate collusive networks of various sizes to win market shares. The popularity of collusive bidding networks affects market fairness and erodes the interests of market players. Although considerable research efforts were made to diagnose collusive bidding networks, there remains a gap in knowledge regarding the relationships bid riggers use to engage in the networks. Therefore, this study used the social network method, where two hundred sixteen collusion cases were collected from China to test these relationships. The results show that collusive bidding networks were characterized by sparseness, a small scale, a high concentration, and strong randomness. Three types of collusive bidding networks were also detected: contractual, spontaneous, and shadow. Furthermore, these collusive bidding networks had discrepancies regarding participants’ identities, forms of collusive bids, and the determination of bid winners. It was found that the proposed social network model of deliberating bid riggers’ relationships lays a solid foundation for the detection of collusive bidding in the construction sector.

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WITHDRAWN: Data leak identification using scattering search K Means in social networks

Cited by 2 publications

References 5 publications

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

A Social Network-Based Examination on Bid Riggers’ Relationships in the Construction Industry: A Case Study of China

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