Cumulative belief peaks evidential K-nearest neighbor clustering

Gong, Chaoyu; Su, Zhi‐gang; Wang, Peihong; Wang, Qian

doi:10.1016/j.knosys.2020.105982

Cited by 28 publications

(6 citation statements)

References 27 publications

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“…Recently, Gong et al 31 raised the issue of uncertainty among members. They introduced a new method called the samples neighboring clustering ensemble (ONCE).…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Liu

Osmani

2022

Trans Emerging Tel Tech

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Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real-world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC-DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared. INTRODUCTIONData mining is the process of discovering regular and hidden patterns and processes in big data and distributed using a wide range of algorithms based on mathematical and statistical sciences. 1,2 These algorithms are usually applied to numeric and nontextual values and textual algorithms are used for textual data. Data mining uses sciences such as artificial intelligence, machine learning, statistics, operational research and database management to build models

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“…Recently, Gong et al 31 raised the issue of uncertainty among members. They introduced a new method called the samples neighboring clustering ensemble (ONCE).…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Liu

Osmani

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…indicate the degree of similarity between two sets of data and indicate different sets of data In recent studies, the KNN-based technique proves its ability to integrate with other DF techniques to improve the clustering process [115]. Yu et al [114] develop a prediction model of short-term traffic conditions using the KNN algorithm.…”

Section: ) K-nearest Neighbormentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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“…Gong et al [28] presented a new evidential clustering algorithm centered on the discovery of "cumulative belief peaks" and the application of the irrefutable K-NN principle. This method's basic assumption is that a cluster center in its neighborhood has the greatest accumulated probability of becoming a cluster center, and that its neighborhood is relatively big.…”

Section: Mijwil and Abttanmentioning

confidence: 99%

Machine Learning Classifiers Based Classification For IRIS Recognition

Chicho

Abdulazeez

Zeebaree

et al. 2021

QAJ

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Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.

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Cumulative belief peaks evidential K-nearest neighbor clustering

Cited by 28 publications

References 27 publications

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Machine Learning Classifiers Based Classification For IRIS Recognition

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