The heavy use of social media platforms is generating a high volume of affective data over the internet. This data is being used by researchers in various domains for prediction, qualitative, and quantitative analytical problems such as stock market prediction, opinion mining of online reviews on products, events, and many more. This article leverages X data for the political response analysis of users towards the 2019 Indian General election. In this article, a methodology is proposed that analyses X data to know what topics were mostly discussed during the election time under the #LoksabhaElection2019 hashtag. Also, we have tried to find out the sentiments of people towards different political terms (words) in the topics inferred. For this task, the study has used topic modeling and sentiment analysis of Tweets. This research may be useful for political parties or newsgroups to mine main topics and analyze the sentiments of people towards different entities.

It remains a challenge in multi-view clustering to effectively integrate heterogeneous views while reducing the impact of noise and redundancy. To tackle this issue, we propose a parameter-free dual-granularity weighted multi-view fuzzy$c$-means clustering framework. The basic idea is to introduce a product-to-one constraint at both the view and attribute levels, enabling adaptive and balanced weight assignment without introducing extra parameters. Two weighting strategies are developed: (i) vector-form weighting, which assigns global importance to views and attributes, and (ii) matrix-form weighting, which further captures cluster-specific relevance. Moreover, both Euclidean and non-Euclidean (exponential transformation) distance measures are incorporated, yielding four algorithmic variants: PDW-MFC-V, PDW-MFC-M, PDW-MAFC-V, and PDW-MAFC-M. Extensive experiments on nine real-world datasets show that our algorithms outperform thirteen related algorithms across multiple metrics. These results confirm that dual-granularity weighting effectively models the relative importance of views and attributes, while the non-Euclidean distance improves robustness to noise. Overall, the proposed framework offers a flexible, parameter-free, and robust solution for multi-view clustering, providing fine-grained interpretability and stable performance across diverse datasets.

Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process. While Evidential Deep Learning (EDL) has been effective in addressing view uncertainty, existing methods predominantly rely on the Dempster-Shafer combination rule, which is sensitive to conflicting evidence and often neglects the critical role of neighborhood structures within multi-view data. To address these limitations, we propose a Trusted Unified Feature-NEighborhood Dynamics (TUNED) model for robust MVC. This method effectively integrates local and global feature-neighborhood (F-N) structures for robust decision-making. Specifically, we begin by extracting local F-N structures within each view. To further mitigate potential uncertainties and conflicts in multi-view fusion, we employ a selective Markov random field that adaptively manages cross-view neighborhood dependencies. Additionally, we employ a shared parameterized evidence extractor that learns global consensus conditioned on local F-N structures, thereby enhancing the global integration of multi-view features. Experiments on benchmark datasets show that our method improves accuracy and robustness over existing approaches, particularly in scenarios with high uncertainty and conflicting views.

Multi-view clustering has become increasingly pervasive and prominent as multiple sources often provide different representations of information. However, existing multi-view clustering algorithms still encounter challenges since most multi-view data do not exhibit clear cluster boundaries, meaning cluster boundaries may locally overlap. Consequently, effectively characterizing and unveiling the imprecise and uncertain cluster structures in multi-view clustering remains an unresolved issue. Inspired by the robust capabilities of neutrosophic clustering in modeling imprecise and uncertain information, this paper introduces two novel multi-view neutrosophic c -means clustering algorithms , which can be regarded as derivatives of NCM in multi-view scenarios. The proposed algorithms are designed to represent the imprecision and uncertainty in cluster assignment of multi-view data while also autonomously discerning the importance of each view to boost clustering performance. We craft two objective functions and develop the corresponding optimization strategies to derive the neutrosophic partition matrix , view weight vector , and cluster centers matrix. Through extensive testing on both synthetic and real-world datasets, we demonstrate the practicality and effectiveness of our proposed algorithms.

Multi-view fuzzy clustering has gained widespread attention due to its unique capability to handle uncertainty through flexible membership assignment, allowing samples to belong to multiple clusters with varying supports, thereby providing a comprehensive understanding of multi-view data. This capability is particularly relevant to knowledge-driven systems that require interpretable integration of multi-view data. However, existing multi-view fuzzy clustering algorithms often struggle with handling noise and incorporating flexible weighting strategies for different views effectively. To address these challenges, this paper proposes four robust multi-view fuzzy clustering algorithms (RMFC-ET-VS, RMFC-ET-VP, RMFC-ET-MS, RMFC-ET-MP), which leverage an exponential transformation of Euclidean distance to effectively mitigate the impact of noise and outliers in the data, thereby enhancing clustering stability. Moreover, we introduce vector-based and matrix-based view weighting strategies, employing sum-to-1 and product-to-1 constraints to ensure that the most informative views contribute more effectively during clustering. The proposed algorithms offer a dual emphasis on robust distance metrics and adaptable view weighting, resulting in more accurate and resilient clustering outcomes. Extensive experiments on multiple real-world datasets demonstrate that the proposed algorithms significantly outperform existing multi-view clustering algorithms, both in terms of clustering performance and robustness. • This paper proposes four robust multi-view fuzzy clustering leveraging exponential transformation. • It introduces vector-based and matrix-based view weighting with sum-to-1 and product-to-1 constraints. • Flexible view-level and cluster-level weight adjustments improve adaptability and accuracy. • Experiments show that the proposed algorithms outperform the existing algorithms.

Multi-view clustering has attracted substantial attention thanks to its ability to integrate information from diverse views. However, the existing methods can only generate hard or fuzzy partitions, which cannot effectively represent the uncertainty and imprecision when facing objects in overlapping clusters, thus increasing the risk of error. To solve the above problems, in this paper, we propose an adaptive weighted multi-view evidential clustering (WMVEC) method based on the theory of belief functions to characterize the uncertainty and imprecision in cluster assignment. Technically, we integrate view weight assignments and credal partition between objects and cluster prototypes into a joint learning framework. The credal partition offers a more comprehensive insight into the data by enabling objects to be associated with not only singleton clusters but also subsets of these clusters (termed meta-clusters) and the empty set, which represents a noise cluster. To avoid the interference of irrelevant and redundant features, we further present a weighted multi-view evidential clustering with feature preference (WMVEC-FP) to learn the importance of each feature under different views. We suggest the objective functions of WMVEC and WMVEC-FP and design alternating optimization schemes to obtain the optimal solutions, respectively. Through an extensive array of experiments, it has been demonstrated that our proposed clustering methods outperform other related and state-of-the-art methods in terms of their advantages and overall effectiveness.

Clustering incomplete instance is still a challenging task since missing values maybe make the cluster information ambiguous, leading to the uncertainty and imprecision in results. This article investigates an enhanced fuzzy clustering with evidence combination method based on Dempster-Shafer theory (DST) to address this problem. First, the dataset is divided into several subsets, and missing values are imputed by neighbors with different weights in each subset. It aims to model missing values locally to reduce the negative impact of the bad estimations. Second, an objective function of enhanced fuzzy clustering is designed and then optimized until the best membership and reliability matrices are found. Each subset has a membership matrix that contains all sub-instances’ membership to different clusters. The fuzzy reliability matrix is employed to characterize the reliability of each subset on different clusters. Third, an adaptive evidence combination rule based on the DST is developed to combine the discounted subresults (memberships) with different reliability to make the final decision for each instance. The proposed method can characterize uncertainty and imprecision by assigning instances to specific clusters or meta-clusters composed of several specific clusters. Once an instance is assigned to a meta-cluster, the cluster information of this instance is (locally) imprecise. The effectiveness of proposed method is demonstrated on several real-world datasets by comparing with existing techniques.

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets.

The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. First, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy$c$-means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets.