Article
Computer Science, Artificial Intelligence
Yuheng Jia, Sirui Tao, Ran Wang, Yongheng Wang
Summary: In this article, a simple and effective CA matrix self-enhancement framework is proposed to improve clustering performance by extracting high-confidence information and propagating it to the CA matrix.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Automation & Control Systems
Keyvan Golalipour, Ebrahim Akbari, Seyed Saeed Hamidi, Malrey Lee, Rasul Enayatifar
Summary: Clustering aims to discover natural groupings of patterns, points, or objects without a deterministic approach to decide the best method for a given set of input data. Clustering ensemble combines computed solutions of base clustering algorithms to achieve stability and robustness, while clustering ensemble selection chooses a subset of base clustering based on quality and diversity for better performance. This survey covers the historical development of data clustering, basic clustering techniques, clustering ensemble algorithms, and clustering ensemble selection techniques for improved quality and diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Environmental Sciences
Soundararaj Vandhana, Jagadeesan Anuradha
Summary: This paper focuses on applying ensemble clustering methods to address air pollution data problems, highlighting the advantage of ensemble algorithms in overcoming bias and variance in traditional clustering processes.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Engineering, Civil
Elnaz Sharghi, Vahid Nourani, Yongqiang Zhang, Parnian Ghaneei
Summary: This study used clustering algorithms to identify patterns of groundwater level and applied artificial intelligence models for multi-step-ahead forecasting. Cluster ensembles and model ensembles were combined to improve the accuracy of predictions. The results showed that the combination of these techniques enhanced the performance of individual methods in forecasting groundwater level.
JOURNAL OF HYDROLOGY
(2022)
Article
Computer Science, Information Systems
Yubo Wang, Shelesh Krishna Saraswat, Iraj Elyasi Komari
Summary: Ensemble clustering, which combines the results of multiple clustering methods, is a challenging research direction in data mining. This study introduces a parallel hierarchical clustering approach using divide-and-conquer strategy to achieve faster and more efficient ensemble clustering. A cluster consensus selection approach is proposed, which selects a subset of primary clusters to participate in the final consensus based on sample-cluster and cluster-cluster similarity. The proposed scheme also incorporates an unsupervised feature selection approach to remove irrelevant features. Extensive evaluations on datasets show that the proposed scheme outperforms state-of-the-art clustering methods, improving average performance by 6% to 24%.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yuxin Zhong, Hongjun Wang, Wenlu Yang, Luqing Wang, Tianrui Li
Summary: This paper proposes a multi-objective genetic model for co-clustering ensemble (GMCCE) that combines fuzzy clustering and hard co-clustering. The model is solved using genetic algorithms, and experiments demonstrate its superior performance compared to other algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Ali Bagherinia, Behrooz Minaei-Bidgoli, Mehdi Hosseinzadeh, Hamid Parvin
Summary: This paper proposes a new fuzzy clustering ensemble framework based on fuzzy cluster-level weighting to improve clustering results by considering the reliability of clusters. Experimental results demonstrate the effectiveness of the proposed approach compared to state-of-the-art methods in terms of evaluation criteria and clustering robustness.
FUZZY SETS AND SYSTEMS
(2021)
Article
Automation & Control Systems
Dong Huang, Chang-Dong Wang, Hongxing Peng, Jianhuang Lai, Chee-Keong Kwoh
Summary: Ensemble clustering based on fast propagation of cluster-wise similarities via random walks addresses the issues of lack of information at higher levels of granularity and neglect of multiscale relationships in current ensemble clustering research. By constructing a cluster similarity graph and conducting random walks, a new cluster-wise similarity matrix is derived to achieve an enhanced co-association matrix. The proposed approach demonstrates effectiveness and efficiency through extensive experiments on various real-world datasets.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Harshal Mittal, Jagarlamudi Sai Laxman, Dheeraj Kumar
Summary: Clustering tendency assessment is a critical problem in exploratory data analysis, and the VAT algorithm provides a visual means to assess it. However, existing automatic clustering tendency assessment methods have poor performance and impractically high run-time. This paper proposes a novel machine learning-based approach that can automatically estimate clustering tendency and infer cluster hierarchy, demonstrating its effectiveness.
Article
Computer Science, Artificial Intelligence
Feijiang Li, Jieting Wang, Yuhua Qian, Guoqing Liu, Keqi Wang
Summary: This article introduces the current status and issues of fuzzy clustering ensemble, and incorporates the idea of prototype-based clustering into fuzzy clustering ensemble, proposing a fuzzy clustering ensemble method based on self-coassociation and prototype propagation.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ting Wu, Yihang Hao, Bo Yang, Lizhi Peng
Summary: Currently, feature selection faces a challenge where no single method can effectively handle various data sets. Ensemble learning is a potential solution, and we propose an ensemble feature selection method based on enhanced co-association matrix (ECM-EFS). We introduce positive-co-association matrix (PCM), negative-co-association matrix (NCM), and relative-co-association matrix (RCM) to discover feature relationships, and use feature kernel for more stable selection. Experimental results show that ECM-EFS provides robust results and reduces computation cost compared to traditional methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Yifan Shi, Zhiwen Yu, C. L. Philip Chen, Huanqiang Zeng
Summary: This article proposes a novel consensus clustering method, CC-CMO, which improves clustering results by optimizing the co-association matrix and considering information from both label space and feature space. The method achieves optimization on global structure and local affinity, and extensive experiments demonstrate its superior performance compared to existing methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qirui Huang, Rui Gao, Hoda Akhavan
Summary: This paper proposes an ensemble hierarchical clustering algorithm based on the cluster consensus selection approach. The algorithm improves the quality of initial clustering by selecting a subset of primary clusters from partitions based on their merit level. Extensive experiments have been conducted to demonstrate the performance of the proposed algorithm.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Ling Zhao, Yunpeng Ma, Shanxiong Chen, Jun Zhou
Summary: This paper proposes a multi-view co-clustering based on multi-similarity. It constructs a graph based on spectral clustering to consider both sample-sample and feature-feature information, and utilizes multiple co-clustering algorithms to calculate similarity information for each view data. Experimental results demonstrate the superiority of the proposed algorithm over existing multi-view co-clustering methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
M. Suleman Basha, S. K. Mouleeswaran, K. Rajendra Prasad
Summary: The clusters assessment in big data clustering is a significant issue, and existing visual models are efficient in evaluating clusters. For high-dimensional big data, hybrid visual computing models incorporating subspace learning techniques can overcome the curse of dimensionality problem.