Medical vision–language pretraining typically relies on static image–text pairs, overlooking temporal cues vital for understanding clinical progression. This limits model sensitivity to evolving semantics and reduces their effectiveness in real-world clinical reasoning. To address this challenge, we propose TAMM—a temporal alignment framework that leverages weak but semantically rich supervision from large language models (LLMs). Given temporally adjacent clinical reports, LLMs automatically generate (i) coarse-grained trend labels (e.g., improving or worsening), and (ii) fine-grained rationales explaining the supporting clinical evidence. These complementary signals inject temporal semantics without requiring manual annotation, and guide vision–language representation learning to capture trend-sensitive cross-modal alignment and rationale-grounded coherence. Experiments on multiple medical benchmarks demonstrate that TAMM improves retrieval and classification performance while yielding more interpretable, temporally consistent embeddings. Our results highlight the potential of leveraging LLM-derived supervision to equip vision–language models with temporal awareness critical for clinical applications.

Graph neural networks (GNNs) have demonstrated impressive performance in a broad spectrum of fields, but always suffer from the generalization problem when confronted with out-of-distribution (OOD) scenarios. Information bottleneck (IB) principle, which endeavors to learn the minimally sufficient representations for downstream tasks, has been shown to be a promising strategy in dealing with this problem. However, the IB-based methods do not inherently distinguish between causal and non-causal parts in the graph, leading to underperforming OOD generalization ability. In this paper, we develop the Graph Causal Information Bottleneck (GCIB) framework, a causal extension of the IB for graph data, which is capable of jointly compressing abundant information and capturing causal dependency from the input graph. Specifically, we endow graph IB with the ability of maintaining causal control by incorporating the underlying causal structure and introducing intervention operation. On this basis, we formulate the learning objective for GCIB and present its specific implementation. Graph representations learned by GCIB can effectively preserve causal information that fundamentally determines graph properties, resulting in outstanding OOD generalization ability. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of GCIB over state-of-the-art baselines.

Graph Neural Networks (GNNs) have achieved remarkable success in analyzing graph-structured data, with their performance dependent on the graph structure. However, models trained on high-quality graph structures often suffer a significant performance drop when evaluated on perturbed graphs. Existing methods tackle this problem by improving the robustness of GNNs, but they often overlook representation deviation caused by structural changes. To address this limitation, we propose an attribute-guided dynamic prompt learning model that generates prompt vectors to approximate the intrinsic information of nodes. With these prompt vectors, the trained GNNs are expected to maintain their performance under perturbed graph structures. Unlike previous prompt-based methods that learn unified prompt vectors for all nodes, we obtain node-level prompts by encoding node attributes that provide unique information. Given the diversity of perturbed graph structures during inference, we introduce a structure-aware adaptation mechanism that adjusts the prompt vectors based on the input graph. Furthermore, we apply gradient-based attacks to generate perturbed graphs, encouraging the model to generalize to unseen structures. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness and robustness of our model.

Graph Neural Networks (GNNs) perform well on in-distribution data but often fail under out-of-distribution (OOD) shifts due to reliance on spurious patterns. To address this, we propose CauVQ, a causal vector quantization framework that improves OOD generalization by identifying and leveraging invariant substructures that are causally predictive. To construct stable and symbolic graph representations, CauVQ decomposes each input into local substructures and maps them to a discrete codebook of prototypical motifs. This enables consistent and interpretable encoding across diverse graph domains. To isolate the causal substructures, we maximize their mutual information with graph labels and refine their representations using a learnable interaction matrix and a causal attention mechanism. Furthermore, we introduce a counterfactual regularization strategy to enforce prediction stability under substructure perturbations, encouraging the model to focus on truly causal patterns rather than superficial shortcuts. Extensive experiments across standard and OOD benchmarks demonstrate that CauVQ consistently outperforms state-of-the-art baselines in robustness and interpretability. Our framework offers a promising step toward reliable, explainable, and distribution-aware graph learning.

Deep multi-view clustering (MVC) methods achieve impressive performance by effectively capturing complementary information across views, where feature fusion serves as the critical mechanism for maximizing cross-view complementarity. However, most existing methods suffer from rigid dependence on non-adaptive predefined fusion operations, resulting in unverifiable and potentially suboptimal fused feature quality. To resolve these limitations, we propose a novel multi-view clustering framework that learns adaptive hierarchical fusion through an unsupervised evolutionary algorithm. Unlike conventional predefined-fusion strategies, our approach employs tree-structured representations (Fusion Trees) for adaptive feature integration. These Fusion Trees are optimized via our evolutionary mechanism, in which models sharing identical architectures but distinct Fusion Trees are conceptualized as evolutionary individuals. Through implementation of the evolutionarily optimized Fusion Tree, the resultant model generates discriminative representations in accordance with biological evolutionary principles. Comprehensive benchmarking across twelve multi-view datasets validates significant performance gains improvement over state-of-the-art baselines.

Adapting Vision Transformers (ViTs) for medical imaging is constrained by the scarcity of data and high-quality annotations, hindering effective training and robust generalization. Visual prompt learning offers a parameter-efficient solution for domain adaptation, but its success depends on accurate and task-relevant semantic guidance-a resource rarely available in real-world clinical practice despite its proven benefits. This motivates the need for mechanisms that can automatically extract reliable semantic cues from existing clinical data. To this end, we propose Graph-Enhanced Visual Prompting (GEVP), the first framework to incorporate cross-modal graph learning into prompt generation for medical imaging. GEVP models image patches and report tokens as graph nodes, captures their spatial and semantic relations via a graph neural network, and produces semantically rich prompts. These prompts are injected into a frozen ViT backbone, guiding attention to diagnostically relevant regions without heavy fine-tuning. A consistent downstream prediction mechanism leverages the pretrained prompt generator to handle both report-available and report-absent settings. Experiments on six public downstream datasets show GEVP surpasses strong prompt- and adapter-based baselines by up to +9.65% F1 on imbalanced tasks and delivers superior unseen disease classification.

Deep learning has achieved remarkable success in supervised image classification tasks, which relies on a large number of labeled samples for each class. Recently, zero-shot learning has garnered significant attention, which aims to recognize unseen classes using only training samples from seen classes. To bridge the gap between images and classes, class semantic attributes are introduced, making the alignment between image and class semantic attributes critical to zero-shot learning. However, existing methods often struggle to accurately focus on the image regions corresponding to individual class semantic attributes and tend to overlook the relations between different regions of an image, leading to poor alignment. To address these challenges, we propose a class semantic attribute perception guided zero-shot learning method. Specifically, we achieve coarse-grained perception of class semantic attributes across the entire image through contrastive semantic learning. Additionally, we attain fine-grained perception of individual class semantic attributes within image regions via region partitioning-based attribute alignment, which fully considers the relations between different regions of an image. By integrating these two processes into a unified network, we achieve multi-grained class semantic attribute perception, thereby enhancing the alignment between images and class semantic attributes. We validate the effectiveness of the proposed method on zero-shot learning benchmark data sets.

Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, Richard Yi Da Xu, Yunya Song, Xian Yang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

In recent years, the out-of-distribution (OOD) generalization problem of graph data has received widespread attention, which refers to how a model maintains strong generalization performance when there are distribution shifts between training and testing data. Invariant learning is considered as an effective solution for solving the OOD generalization problem. However, graph data has more complex structures than Euclidean data and contains a variety of distribution shifts. Therefore, the captured invariant features may include spurious invariant components, which can drop the generalization performance of the model. To solve this problem, we propose a new invariant learning model named causal information bottleneck learning model (CIBL). Specifically, we introduce information bottleneck principle to eliminate the impact of spurious invariant components on the model by compressing the learned redundant information. In this way, the model can learn causal invariant features, which can ensure the generalization performance of the model. We conduct several experiments in different OOD scenarios, and the results demonstrate that the CIBL model outperforms other baseline models.

Graph contrastive learning (GraphCL) is widely used in recommendation systems. However, GraphCL recommendation systems are still plagued by the popularity bias problem. Besides, during the contrastive task, data augmentation may damage the original data structure information. To solve these problems, we propose a new GraphCL recommendation model, namely GCR-DDA, which contains three key modules: collaborative relation encoder (CRE), debiasing channel and structure preserving channel. The CRE is used to integrate local neighborhood information. In the debiasing channel, noise-based embedding augmentation is designed to mitigate the popularity bias problem of the skewed distribution. In the structure preserving channel, the variational graph auto-encoder (VGAE) is implied as a graph generation model for data augmentation. Contrastive task is constructed from the two channels, and is integrated with the recommendation task in a joint optimization model. Results of extensive experiments on three datasets show that GCR-DDA outperforms baselines.

Graph Neural Network (GNN) is a popular semi-supervised graph representation learning method, whose performance strongly relies on the quality and quantity of labeled nodes. Given the insufficiency of labeled nodes in many real applications, many multi-channel GNNs have been developed to extract self-supervised information by leveraging consistency and complementarity among augmented graphs from different channels. However, these methods often struggle to balance conflicting self-supervised constraints, enhancing certain types of information at the expense of others. To tackle this problem, we propose a Multi-channel Disentangled Graph Neural Network (MD-GraphNet), which effectively classifies self-supervised constraints by learning disentangled representations. Specifically, our model enforces consistency constraints for shared representations, graph reconstruction constraints for complementary (or private) representations, and aligning constraints for fused representations. Our model overcomes the confusion and loss problems of different types of self-supervised signals. Experimental results on benchmark datasets demonstrate the effectiveness of MD-GraphNet for semi-supervised node classification.

Adapting Vision Transformers (ViTs) to medical image analysis is challenging due to the scarcity of annotated data and the significant domain shift from natural to medical images. Traditional fine-tuning approaches, while effective, require storing separate model parameters for each task, leading to high computational costs. Existing prompt tuning methods reduce this overhead by introducing task-specific prompt tokens, but they often fail to fully leverage label semantics, resulting in suboptimal performance for medical tasks. To address these limitations, we propose a label-semantic-based prompt tuning method (LPT), which transforms the visual prompt learning problem into a text-image alignment task. Unlike traditional prompt methods that only focus on visual prompts, LPT incorporates label semantics through a cross-attention-based module to better align image features with the target labels. This approach not only captures rich semantic information from the labels but also enhances the model’s ability to extract fine-grained image details relevant to specific medical conditions. By leveraging label-text alignment during training, LPT improves both label utilization and model adaptability, enabling more accurate predictions. Extensive experiments on eight diverse medical datasets demonstrate that LPT significantly improves diagnostic accuracy and generalization, outperforming both traditional fine-tuning and current prompt-based methods, especially in data-limited scenarios.

Contrastive Learning (CL) is an effective self-supervised learning method. It performs instance-level contrastiveness based on the image representations, which enables the model to extract abstract information from images. However, when training data is insufficient, abstract information fails to distinguish samples from different classes. This problem is more severe in the pre-training of Vision Transformer (ViT). In general, detailed information is crucial for enhancing the discrimination of representations. Patch representations, which focus on the details of images, are often overlooked in existing methods that train ViT through CL, resulting in the confusion of similar samples. To address this problem, we propose a Contrastive Learning model with Enhancing Detailed Information (CL-EDI) for pre-training ViT. Our model consists of dual ViT contrastive modules. The first module is similar to MoCo V3, which can learn abstract information about images. The role of the second ViT contrastive module is to enhance detailed information in data representations by aggregating patch representations of images. Extensive experiments demonstrate the necessity of learning detailed information. Across several datasets, our model surpasses existing approaches in image classification, transfer learning and object detection tasks.

Establishing local semantic correspondences between medical images and their corresponding reports is crucial for effective medical vision-language pre-training. However, existing methods encounter two major challenges: (1) lesion regions in radiological images are often small, blurry, or lack clear boundaries, complicating accurate localization; and (2) medical reports typically contain redundant or non-diagnostic words, hindering precise semantic alignment. To overcome these issues, we propose MedAligner, a specialized local alignment network for medical vision-language pre-training. MedAligner employs dual encoders to extract both global and local representations and uses global contrastive learning to maintain coarse semantic consistency. To enhance local alignment, we introduce a Word-Region Alignment, which generates a learnable word-pixel similarity matrix that is sparsified to identify salient lesion regions accurately. Additionally, our Diagnostic Term Filtering dynamically samples high-importance diagnostic terms from reports, aligning them with identified lesion areas via a local contrastive loss. Importantly, we adopt a progressive training strategy that gradually refines both the input text and semantic alignment. This is achieved by reconstructing concise diagnostic reports and progressively updating word-pixel similarity, generating increasingly accurate image-text pairs. Extensive experiments demonstrate that MedAligner significantly surpasses existing approaches on tasks such as phrase grounding, image-text retrieval, and zero-shot classification, setting new benchmarks in medical vision-language pre-training.

Graph Contrastive Learning (GCL) plays a crucial role in multimedia applications due to its effectiveness in analyzing graph-structured data. Existing GCL methods focus on maximizing the agreement of node representations across different augmentations, which leads to the neglect of unique and complementary information in each augmentation. In this paper, we propose a fusion-based GCL model (FB-GCL) that learns fused representations to effectively capture complementary information from both the graph structure and node attributes. Our model consists of two modules: a graph fusion encoder and a graph contrastive module. The graph fusion encoder adaptively fuses the representations learned from the topology graph and the attribute graph. The graph contrastive module extracts supervision signals from the raw graph by leveraging both the pairwise relationships within the graph structure and the multi-label information from the attributes. Extensive experiments on seven benchmark datasets demonstrate that FB-GCL enhances performance in node classification and link prediction tasks. This improvement is especially valuable for multimedia data analysis, as integrating graph structure and attribute information is crucial for effectively understanding and processing complex datasets.

The increasing interest in learning from paired medical images and textual reports highlights the need for methods that can achieve multi-grained alignment between these two modalities. However, most existing approaches overlook finegrained semantic alignment, which can constrain the quality of the generated representations. To tackle this problem, we propose the Multi-Grained Vision-and-Language Alignment (MGVLA) model, which effectively leverages multi-grained correspondences between medical images and texts at different levels, including disease, instance, and token levels. For disease-level alignment, our approach adopts the concept of contrastive learning and uses medical terminologies detected from textual reports as soft labels to guide the alignment process. At the instance level, we propose a strategy for sampling hard negatives, where images and texts with the same disease type but differing in details such as disease locations and severity are considered as hard negatives. This strategy helps our approach to better distinguish between positive and negative image-text pairs, ultimately enhancing the quality of our learned representations. For token-level alignment, we employ a masking and recovery technique to achieve finegrained semantic alignment between patches and sub-words. This approach effectively aligns the different levels of granularity between the image and language modalities. To assess the efficacy of our MGVLA model, we conduct comprehensive experiments on the image-text retrieval and phrase grounding tasks.

In this article, a new unsupervised contrastive clustering (CC) model is introduced, namely, image CC with self-learning pairwise constraints (ICC-SPC). This model is designed to integrate pairwise constraints into the CC process, enhancing the latent representation learning and improving clustering results for image data. The incorporation of pairwise constraints helps reduce the impact of false negatives and false positives in contrastive learning, while maintaining robust cluster discrimination. However, obtaining prior pairwise constraints from unlabeled data directly is quite challenging in unsupervised scenarios. To address this issue, ICC-SPC designs a pairwise constraints learning module. This module autonomously learns pairwise constraints among data samples by leveraging consensus information between latent representation and pseudo-labels, which are generated by the clustering algorithm. Consequently, there is no requirement for labeled images, offering a practical resolution to the challenge posed by the lack of sufficient supervised information in unsupervised clustering tasks. ICC-SPC's effectiveness is validated through evaluations on multiple benchmark datasets. This contribution is significant, as we present a novel framework for unsupervised clustering by integrating contrastive learning with self-learning pairwise constraints.

Electronic health records (EHRs) are a valuable source of information that can aid in understanding a patient’s health condition and making informed healthcare decisions. However, modelling longitudinal EHRs with heterogeneous information is a challenging task. Although recurrent neural networks (RNNs), which are current artificial intelligence (AI) models, have the capability to capture longitudinal information, their explanatory power is limited. Predictive clustering is a recent development in this field, which provides cluster-level explainable evidence for disease risk prediction. Nonetheless, the challenge of determining the optimal number of clusters has put a brake on the widespread application of predictive clustering for disease risk prediction. In this paper, we introduce a novel non-parametric predictive clustering-based risk prediction model that integrates the Dirichlet Process Mixture Model (DPMM) with predictive clustering via neural networks. To enhance the model’s interpretability, we integrate attention mechanisms that enable the capture of local-level evidence in addition to the cluster-level evidence provided by predictive clustering. The outcome of this research is the development of a multi-level explainable artificial intelligence (AI) model. We evaluated the proposed model on two real-world datasets and demonstrated its effectiveness in capturing longitudinal EHR information for disease risk prediction. Additionally, the model was successful in generating explainable evidence to support its predictions.

Electronic health records (EHRs) contain vast medical information like diagnosis, medication, and procedures, enabling personalized drug recommendations and treatment adjustments. However, current drug recommendation methods only model patients' health conditions from EHR data, neglecting the rich relationships within the data. This paper seeks to utilize a heterogeneous information network (HIN) to represent EHR and develop a graph representation learning method for medication recommendation. However, three critical issues need to be investigated: (1) co-occurrence of diagnosis and drug for the same patient does not imply their relevance; (2) patients' directly associated information may not be sufficient to reflect their health conditions; and (3) the cold start problem exists when patients have no historical EHRs. To tackle these challenges, we develop a bi-channel heterogeneous local structural encoder to decouple and extract the diverse information in HIN. Additionally, a global information capture and fusion module, aggregating meta-paths to form a global representation, is introduced to fill the information gaps in records. A longitudinal model using rich structural information available in EHR data is proposed for drug recommendations to new patients. Experimental results on real-world EHR data demonstrate significant improvements over existing approaches.

Consensus clustering is to find a high quality and robust partition that is in agreement with multiple existing base clusterings. However, its computational cost is often very expensive and the quality of the final clustering is easily affected by uncertain consensus relations between clusters. In order to solve these problems, we develop a new -type algorithm, called -relations-based consensus clustering with double entropy-norm regularizers (KRCC-DE). In this algorithm, we build an optimization model to learn a consensus-relation matrix between final and base clusters and employ double entropy-norm regularizers to control the distribution of these consensus relations, which can reduce the impact of the uncertain consensus relations. The proposed algorithm uses an iterative strategy with strict updating formulas to get the optimal solution. Since its computation complexity is linear with the number of objects, base clusters, or final clusters, it can take low computational costs to effectively solve the consensus clustering problem. In experimental analysis, we compared the proposed algorithm with other -type-based and global-search consensus clustering algorithms on benchmark datasets. The experimental results illustrate that the proposed algorithm can balance the quality of the final clustering and its computational cost well.

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods to capture complex clusters in data. Some additional prior information can help it to further reduce the difference between its clustering results and users' expectations. However, it is hard to get the prior information under unsupervised scene to guide the clustering process. To solve this problem, we propose a self-constrained spectral clustering algorithm. In this algorithm, we extend the objective function of spectral clustering by adding pairwise and label self-constrained terms to it. We provide the theoretical analysis to show the roles of the self-constrained terms and the extensibility of the proposed algorithm. Based on the new objective function, we build an optimization model for self-constrained spectral clustering so that we can simultaneously learn the clustering results and constraints. Furthermore, we propose an iterative method to solve the new optimization problem. Compared to other existing versions of spectral clustering algorithms, the new algorithm can discover a high-quality cluster structure of a data set without prior information. Extensive experiments on benchmark data sets illustrate the effectiveness of the proposed algorithm.

Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters.

Zero-shot node classification is a very important challenge for classical semi-supervised node classification algorithms, such as Graph Convolutional Network (GCN) which has been widely applied to node classification. In order to predict the unlabeled nodes from unseen classes, zero-shot node classification needs to transfer knowledge from seen classes to unseen classes. It is crucial to consider the relations between the classes in zero-shot node classification. However, the GCN only considers the relations between the nodes, not the relations between the classes. Therefore, the GCN can not handle the zero-shot node classification effectively. This paper proposes a Dual Bidirectional Graph Convolutional Networks (DBiGCN) that consists of dual BiGCNs from the perspective of the nodes and the classes, respectively. The BiGCN can integrate the relations between the nodes and between the classes simultaneously in an united network. In addition, to make the dual BiGCNs work collaboratively, a label consistency loss is introduced, which can achieve mutual guidance and mutual improvement between the dual BiGCNs. Finally, the experimental results on real-world graph data sets verify the effectiveness of the proposed method.

Semi-supervised clustering is one of important research topics in cluster analysis, which uses pre-given knowledge as constraints to improve the clustering performance. While clustering a data set, people often get prior constraints from different information sources, which may have different representations and contents, to guide clustering process. However, most of existing semi-supervised clustering algorithms are based on single-source constraints and rarely consider to integrate multi-source constraints to enhance the clustering quality. To solve the problem, we analyze the relations among different types of constraints and propose an uniform representation for them. Based it, we propose a new semi-supervised clustering algorithm to find out a clustering that has good cluster structure and high consensus of all the sources of constraints. In the algorithm, we construct an optimization objective model and its solution method to achieve the aim. This algorithm can integrate multi-source constraints well to reduce the effect of incorrect constraints from single sources and find out a high-quality clustering. By the experimental studies on several benchmark data sets, we illustrate the effectiveness of the proposed algorithm, compared to other semi-supervised clustering algorithms.

Due to the complex structure of the real-world data, nonlinearly separable clustering is one of popular and widely studied clustering problems. Currently, various types of algorithms, such as kernel k-means, spectral clustering and density clustering, have been developed to solve this problem. However, it is difficult for them to balance the efficiency and effectiveness of clustering, which limits their real applications. To get rid of the deficiency, we propose a three-level optimization model for nonlinearly separable clustering which divides the clustering problem into three sub-problems: a linearly separable clustering on the object set, a nonlinearly separable clustering on the cluster set and an ensemble clustering on the partition set. An iterative algorithm is proposed to solve the optimization problem. The proposed algorithm can use low computational cost to effectively recognize nonlinearly separable clusters. The performance of this algorithm has been studied on synthetical and real data sets. Comparisons with other nonlinearly separable clustering algorithms illustrate the efficiency and effectiveness of the proposed algorithm.

In this paper, we provide an explicit theoretical connection between Sparse subspace clustering (SSC) and spectral clustering (SC) from the perspective of learning a data similarity matrix. We show that spectral clustering with Gaussian kernel can be viewed as sparse subspace clustering with entropy-norm (SSC+E). Compared to SSC, SSC+E can obtain an analytical, symmetrical, nonnegative and nonlinearly-representational similarity matrix. Besides, SSC+E makes use of Gaussian kernel to compute the sparse similarity matrix of objects, which can avoid the complex computation of the sparse optimization program of SSC. Finally, we provide the experimental analysis to compare the efficiency and effectiveness of sparse subspace clustering and spectral clustering on ten benchmark data sets. The theoretical and experimental analysis can well help users for the selection of high-dimensional data clustering algorithms.

Cluster ensemble is a very important tool that aggregates several base clusterings to generate a single output clustering with improved robustness and stability. However, the quality of the final clustering is often affected by uncertainties on the generation and integration of base clusterings. In this paper, we develop an information-theoretical framework which makes an effort to obtain a final clustering with high consensus on both the original data set and the base clustering set by minimizing the two uncertainties of cluster ensemble. In this framework, we provide a weighted consensus measure based on information entropy to evaluate the quality of a clustering, the similarity between clusters and the similarity between objects. Based on the measure, we propose three weighted cluster ensemble algorithms with different ensemble strategies in the framework, including the weighted feature consensus algorithm, the weighted relabeling consensus algorithm and the weighted pairwise-similarity consensus algorithm. In the experimental analysis, we compare the proposed algorithms with other existing clustering ensemble algorithms on several data sets. The comparison results illustrate the proposed algorithms are very effective and robust.

Fuzzy cluster ensemble is an important research component of ensemble learning, which is used to aggregate several fuzzy base clusterings to generate a single output clustering with improved robustness and quality. However, since clustering is unsupervised, where “accuracy” does not have a clear meaning, it is difficult for existing ensemble methods to integrate multiple fuzzy k-means clusterings to find arbitrarily shaped clusters. To overcome the deficiency, we propose a new ensemble clusterer (algorithm) of multiple fuzzy k-means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of local-credible memberships from a base clustering, the production of multiple base clusterings with different local-credible spaces, and the construction of cluster relation based on indirect overlap of local-credible spaces. The proposed ensemble clusterer not only inherits the scalability of fuzzy k-means but also overcomes the inability to find arbitrarily shaped clusters. We compare the proposed algorithm with other cluster ensemble algorithms on several synthetical and real datasets. The experimental results illustrate the effectiveness and efficiency of the proposed algorithm.

There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the clustering model while each new input data subset is flowing. It simultaneously considers the certainty of the clustering model and the continuity with the last clustering model in the clustering process. An iterative optimization algorithm is proposed to solve an optimal solution of the objective function with some constraints. Furthermore, we strictly derive a detection index for drifting concepts from the optimization model. We propose a detection method that integrates the detection index and the optimization model to catch the evolution trend of cluster structures on a categorical data stream. The new method can effectively avoid ignoring the effect of the clustering validity on the detection result. Finally, using the experimental studies on several real data sets, we illustrate the effectiveness of the proposed algorithm in clustering categorical data streams, compared with existing data-streams clustering algorithms.

As a leading partitional clustering technique, k-modes is one of the most computationally efficient clustering methods for categorical data. In the k-modes, a cluster is represented by a "mode," which is composed of the attribute value that occurs most frequently in each attribute domain of the cluster, whereas, in real applications, using only one attribute value in each attribute to represent a cluster may not be adequate as it could in turn affect the accuracy of data analysis. To get rid of this deficiency, several modified clustering algorithms were developed by assigning appropriate weights to several attribute values in each attribute. Although these modified algorithms are quite effective, their convergence proofs are lacking. In this paper, we analyze their convergence property and prove that they cannot guarantee to converge under their optimization frameworks unless they degrade to the original k-modes type algorithms. Furthermore, we propose two different modified algorithms with weighted cluster prototypes to overcome the shortcomings of these existing algorithms. We rigorously derive updating formulas for the proposed algorithms and prove the convergence of the proposed algorithms. The experimental studies show that the proposed algorithms are effective and efficient for large categorical datasets.

$K$-means is a partitional clustering technique that is well-known and widely used for its low computational cost. The representative algorithms include the hard$k$-means and the fuzzy$k$-means. However, the performance of these algorithms tends to be affected by skewed data distributions, i.e., imbalanced data. They often produce clusters of relatively uniform sizes, even if input data have varied cluster sizes, which is called the “uniform effect.” In this paper, we analyze the causes of this effect and illustrate that it probably occurs more in the fuzzy$k$-means clustering process than the hard$k$-means clustering process. As the fuzzy index$m$increases, the “uniform effect” becomes evident. To prevent the effect of the “uniform effect,” we propose a multicenter clustering algorithm in which multicenters are used to represent each cluster, instead of one single center. The proposed algorithm consists of the three subalgorithms: the fast global fuzzy$k$-means, Best M-Plot, and grouping multicenter algorithms. They will be, respectively, used to address the three important problems: 1) How are the reliable cluster centers from a dataset obtained? 2) How are the number of clusters which these obtained cluster centers represent determined? 3) How is it judged as to which cluster centers represent the same clusters? The experimental studies on both synthetic and real datasets illustrate the effectiveness of the proposed clustering algorithm in clustering balanced and imbalanced data.

A fundamental assumption often made in unsupervised learning is that the problem is static, i.e., the description of the classes does not change with time. However, many practical clustering tasks involve changing environments. It is hence recognized that the methods and techniques to analyze the evolving trends for changing environments are of increasing interest and importance. Although the problem of clustering numerical time-evolving data is well-explored, the problem of clustering categorical time-evolving data remains as a challenging issue. In this paper, we propose a generalized clustering framework for categorical time-evolving data, which is composed of three algorithms: a drifting-concept detecting algorithm that detects the difference between the current sliding window and the last sliding window, a data-labeling algorithm that decides the most-appropriate cluster label for each object of the current sliding window based on the clustering results of the last sliding window, and a cluster-relationship-analysis algorithm that analyzes the relationship between clustering results at different time stamps. The time-complexity analysis indicates that these proposed algorithms are effective for large datasets. Experiments on a real dataset show that the proposed framework not only accurately detects the drifting concepts but also attains clustering results of better quality. Furthermore, compared with the other framework, the proposed one needs fewer parameters, which is favorable for specific applications.