Incomplete Clustering Research Articles

Bioinspired Hybrid and Incomplete Data Clustering

Enhancing the results of data clustering for hybrid (numeric and categorical) and missing data is paramount for robust pattern recognition and decision-making in various fields. Traditional clustering algorithms often need help with heterogeneous data types and incomplete information, leading to suboptimal groupings and potentially biased insights. By addressing these challenges, advanced techniques, such as bioinspired algorithms, can provide more accurate and comprehensive clustering. We show the usefulness of using internal validity indices in obtaining high-quality clusters for hybrid and incomplete data. In addition, this work analyzes the influence of applying the recently proposed PAntSA on the performance of hybrid and incomplete clustering algorithms. To do this, the results of different algorithms are compared before and after applying PAntSA. The statistical analysis of the results provides experimental evidence that supports that the PAntSA algorithm improves the quality of the groups obtained by hybrid and incomplete data clustering methods.

Tensorized Incomplete Multi-view Kernel Subspace Clustering

Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are often faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structures in the multiple kernel spaces. Second, they usually neglect the high-order relationship in multiple representations. Third, they often have two or even more hyper-parameters and may not be practical for some real-world applications. To tackle these issues, we present a Tensorized Incomplete Multi-view Kernel Subspace Clustering (TIMKSC) approach. Specifically, by incorporating the kernel learning technique into an incomplete subspace clustering framework, our approach can robustly explore the latent subspace structure hidden in multiple views. Furthermore, we impute the incomplete kernel matrices and learn the low-rank tensor representations in a mutual enhancement manner. Notably, our approach can discover the underlying relationship among the observed and missing samples while capturing the high-order correlation to assist subspace clustering. To solve the proposed optimization model, we design a three-step algorithm to efficiently minimize the unified objective function, which only involves one hyper-parameter that requires tuning. Experiments on various benchmark datasets demonstrate the superiority of our approach. The source code and datasets are available at: https://www.researchgate.net/publication/381828300_TIMKSC_20240629.

Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering

Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering

Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding

Incomplete multi-view clustering becomes an important research problem, since multi-view data with missing values are ubiquitous in real-world applications. Although great efforts have been made for incomplete multi-view clustering, there are still some challenges: 1) most existing methods didn't make full use of multi-view information to deal with missing values; 2) most methods just employ the consistent information within multi-view data but ignore the complementary information; 3) For the existing incomplete multi-view clustering methods, incomplete multi-view representation learning and clustering are treated as independent processes, which leads to performance gap. In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information while instance-level contrastive learning for latent representation is designed to employ the consistent information. Thirdly, an end-to-end framework is proposed to integrate multi-view missing values handling, multi-view representation learning and clustering assignment for joint optimization. Experiments compared with state-of-the-art approaches demonstrated the effectiveness and superiority of our method. Our code is publicly available at https://github.com/liunian-Jay/ICMVC. The version with supplementary material can be found at http://arxiv.org/abs/2312.08697.

Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoT

With the wide deployment of the Internet of Things (IoT), large volumes of incomplete multiview data that violates data integrity is generated by various applications, which inevitably produces negative impacts on the quality of service of IoT systems. Incomplete multiview clustering (IMC), as an essential technique of data processing, has the potential for mining patterns of incomplete IoT data. However, previous methods utilize notion-strong distances that can only measure differences between distributions at the overlap of data manifolds in fusing complementary information of data for pattern mining. They may suffer from biased estimation and information loss in capturing intrinsic structures of incomplete multiview data. To address these challenges, a semidiscrete multiview optimal transport (SD-MOT) is defined for IMC, which utilizes distances with weak notions to capture intrinsic structures of incomplete multiview data. Specifically, IMC is recast as an equivalent optimal transport between continuous incomplete multiview data and discrete clustering centroids, to avoid the strict assumption on overlap between manifolds in pattern mining. Then, SD-MOT is instantiated as a deep incomplete contrastive clustering network to remedy biased estimation and information loss on intrinsic structures of incomplete multiview data. Afterwards, a variational solution to SD-MOT is derived to effectively train the network parameters for pattern mining. Finally, extensive experiments on four representative incomplete multiview datasets verify the superiority of SD-MOT in comparison with nine baseline methods.

A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making.

Clustering is an effective means to reduce the scaling of large-scale group decision-making (LSGDM). However, there are many problems with clustering methods, such as incomplete or ambiguous information usually provided by different decision makers. Traditional clustering methods may not be able to handle these situations effectively, resulting in incomplete decision-making information. Calculating the clustering centers may become very complex and time-consuming. Inappropriate distance weights may also lead to incorrect cluster assignments, and these problems will seriously affect the clustering results. This research provides a novel incomplete hesitant fuzzy information supplement and clustering approach for large-scale group decision-making in order to address the aforementioned difficulties. First, the approach takes into account the trust degradation and the inhibition of relationships of distrust in the process of trust propagation, and then it builds a global and local network of trust. A novel supplemental formula is provided that takes into account the decision-preference maker's as well as the trust-neighbor's information, allowing the decision-neighbor maker's recommendation to be realized. Therefore, an improved distance function can be proposed to calculate the weights by combining the relative standard deviation theory and selecting the selected clustering centers by using the density peaks in order to optimize the selection of clustering centers and reduce the complexity and scaling of the decision. Finally, an example is presented to demonstrate how the proposed method can be applied. The consistency index and comparison experiments are used to evaluate if the suggested approach is effective and reliable.

Obstructed Defecation Syndrome in Young Women

Obstructed Defecation Syndrome (ODS) is characterized by excessive straining at stool, incomplete rectal evacuation, and needing for perineal splinting. This condition is prevalent in young women suffering from constipation, approximating 7-15% [6]. ODS is refractory to laxative use. Typically, the block is found in the rectosigmoid portion. The main causes are divided into mechanical and functional. Mechanical causes interfere with stool passage, as can be found for instance in rectal prolapse. Functional causes include various neurologic or behavioral disorders, leading to pelvic floor dysfunction, discoordination of the defecatory process, and impaired rectal sensation. In dyssynergic defecation, that affects many young women with chronic constipation, there is an inability to coordinate the abdominal and pelvic floor muscles to evacuate stools. Paradoxical contraction of the pelvic floor muscles during defecation hinders evacuation [10-12]. ODS is characterized by persistent sensation of rectal fullness and painful prolonged or excessive straining, accompanied by a sensation of incomplete evacuation and clustering, often, digital manipulation. Hemorrhoids and fissures are also related to functional ODS due to the psychological condition to avoid pain during defecation. A holistic approach is needed for patients with ODS, considering that most of these young women present with psychological distress, either anxiety or depression. Anismus affects many ODS patients [8].

A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion.

Incomplete pattern clustering is a challenging task because the unknown attributes of the missing data introduce uncertain information that affects the accuracy of the results. In addition, the clustering method based on the single view ignores the complementary information from multiple views. Therefore, a new belief two-level weighted clustering method based on multiview fusion (BTC-MV) is proposed to deal with incomplete patterns. Initially, the BTC-MV method estimates the missing data by an attribute-level weighted imputation method with k-nearest neighbor (KNN) strategy based on multiple views. The unknown attributes are replaced by the average of the KNN. Then, the clustering method based on multiple views is proposed for a complete data set with estimations; the view weights represent the reliability of the evidence from different source spaces. The membership values from multiple views, which indicate the probability of the pattern belonging to different categories, reduce the risk of misclustering. Finally, a view-level weighted fusion strategy based on the belief function theory is proposed to integrate the membership values from different source spaces, which improves the accuracy of the clustering task. To validate the performance of the BTC-MV method, extensive experiments are conducted to compare with classical methods, such as MI-KM, MI-KMVC, KNNI-FCM, and KNNI-MFCM. Results on six UCI data sets show that the error rate of the BTC-MV method is lower than that of the other methods. Therefore, it can be concluded that the BTC-MV method has superior performance in dealing with incomplete patterns.

Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering

Aiming at solving the problem of clustering in the multi-view datasets which include samples with information missing in one or more views, incomplete multi-view clustering has received considerable attention. However, most studies can not get satisfying accuracy and efficiency when dealing with datasets in which a considerable number of instances are missing in partial views. To address this problem, a method named Auto-weighted Sample-level Fusion with Anchors for Incomplete Multi-view Clustering (ASA-IC) is proposed in this paper. It designs an auto-weighted sample-level fusion strategy, which realizes the optimized conversion from the individual instance-to-anchor similarity learning to the concensus instance-to-anchor similarity matrix construction. ASA-IC can not only handle incomplete samples and effectively explore the relationship between each instance and anchors, but also deal with various incomplete clustering situations and be applied in large-scale datasets as well. Besides, experiments on 5 complete datasets and 27 incomplete ones illustrate its effectiveness quantitatively and qualitatively.

Incomplete clustering analysis via multiple imputation

Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.

Complete/incomplete multi‐view subspace clustering via soft block‐diagonal‐induced regulariser

This study proposes a novel multi-view soft block diagonal representation framework for clustering complete and incomplete multi-view data. First, given that the multi-view self-representation model offers better performance in exploring the intrinsic structure of multi-view data, it can be nicely adopted to individually construct a graph for each view. Second, since an ideal block diagonal graph is beneficial for clustering, a ‘soft’ block diagonal affinity matrix is constructed by fusing multiple previous graphs. The soft diagonal block regulariser encourages a matrix to approximately have (not exactly) K diagonal blocks, where K is the number of clusters. This strategy adds robustness to noise and outliers. Third, to handle incomplete multi-view data, multiple indicator matrices are utilised, which can mark the position of missing elements of each view. Finally, the alternative direction of multipliers algorithm is employed to optimise the proposed model, and the corresponding algorithm complexity and convergence are also analysed. Extensive experimental results on several real-world datasets achieve the best performance among the state-of-the-art complete and incomplete clustering methods, which proves the effectiveness of the proposed methods.

ICmSC: Incomplete Cross-Modal Subspace Clustering.

Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering (i.e., iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a l1,2 -norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.

Consensus guided incomplete multi-view spectral clustering

Incomplete multi-view clustering which aims to solve the difficult clustering challenge on incomplete multi-view data collected from diverse domains with missing views has drawn considerable attention in recent years. In this paper, we propose a novel method, called consensus guided incomplete multi-view spectral clustering (CGIMVSC), to address the incomplete clustering problem. Specifically, CGIMVSC seeks to explore the local information within every single-view and the semantic consistent information shared by all views in a unified framework simultaneously, where the local structure is adaptively obtained from the incomplete data rather than pre-constructed via a k-nearest neighbor approach in the existing methods. Considering the semantic consistency of multiple views, CGIMVSC introduces a co-regularization constraint to minimize the disagreement between the common representation and the individual representations with respect to different views, such that all views will obtain a consensus clustering result. Experimental comparisons with some state-of-the-art methods on seven datasets validate the effectiveness of the proposed method on incomplete multi-view clustering.

Multi-view cluster analysis with incomplete data to understand treatment effects

Multi-view cluster analysis, as a popular granular computing method, aims to partition sample subjects into consistent clusters across different views in which the subjects are characterized. Frequently, data entries can be missing from some of the views. The latest multi-view co-clustering methods cannot effectively deal with incomplete data, especially when there are mixed patterns of missing values. We propose an enhanced formulation for a family of multi-view co-clustering methods to cope with the missing data problem by introducing an indicator matrix whose elements indicate which data entries are observed and assessing cluster validity only on observed entries. In comparison with common methods that impute missing data in order to use regular multi-view analytics, our approach is less sensitive to imputation uncertainty. In comparison with other state-of-the-art multi-view incomplete clustering methods, our approach is sensible in the cases of either missing any entry in a view or missing the entire view. We first validated the proposed strategy in simulations, and then applied it to a treatment study of opioid dependence which would have been impossible with previous methods due to a number of missing-data patterns. Patients in the treatment study were naturally assessed in different feature spaces such as in the pre-, during- and post-treatment time windows. Our algorithm was able to identify subgroups where patients in each group showed similarities in all of the three time windows, thus leading to the identification of pre-treatment (baseline) features predictive of post-treatment outcomes. We found that cue-induced heroin craving predicts adherence to XR-NTX therapy. This finding is consistent with the clinical literature, serving to validate our approach.

An improved incomplete AP clustering algorithm based on K nearest neighbours

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

An improved incomplete AP clustering algorithm based on K nearest neighbours

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

Late Fusion Incomplete Multi-View Clustering.

Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel $k$k-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint optimization problem where the imputation and clustering are alternatively performed until convergence. However, the comparatively intensive computational and storage complexities preclude it from practical applications. To address these issues, we propose Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generated by incomplete views. Specifically, our algorithm jointly learns a consensus clustering matrix, imputes each incomplete base matrix, and optimizes the corresponding permutation matrices. We develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed LF-IMVC in terms of clustering accuracy, running time, advantages of late fusion multi-view clustering, evolution of the learned consensus clustering matrix, parameter sensitivity and convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.

Land-allocation and clan-formation in modern residential developments in Oman

Affordable housing with fair and transparent land-allocation measures is a challenge for many countries. The Sultanate of Oman solves this issue by means of land-allocation lotteries in which male and female citizens can participate in order to win a parcel of a new housing area. Besides supporting those in financial need the lottery aims to mix the separate tribes of Oman and thus contribute to the nation building of a modern society. In an earlier work, we showed that there must be severe doubts about the economic support aspect as it seems that the current lottery, without additional measures, fuels private land speculation, see Heim et al. (Homo Oecon 35:1–30, 2018). This article studies the emergent phenomenon of clan formation within the specific land-allocation system of Oman with a new mathematical and computational approach of agent-based modelling. This model is used to base the criticism to the system on scientific evidence. Both the method (clan formation exemplified as agent-based model) and findings (simulated agents and real-world behaviour) advance the discussion on how to mitigate some of the negative effects associated with the current land allocation system. To do so, we discuss the current state and criticism around this aspect of urbanisation in Oman. We study the spatial mixing of the tribes and the possibility of clan formation in modern Omani settlements in particular Muscat Capital Area. We see emergent phenomena arising from the agent-based model that matches real world behaviour leading to negative effects such as partial and incomplete land development, tribal clustering and real-estate market distortion. The agent-based model is used to discuss alternative scenarios under which tribal clan formation is used to generate a fairer and more complete settlement process in Oman to ensure the intended outcomes of Oman’s land-allocation lottery system.

A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine

Data imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence matrix strategies to measure correlation between records and attributes, respectively. Finally, quantifiable correlation is converted to weights for imputation. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.

Incomplete clustering of electricity consumption: an empirical analysis with industrial and residential datasets

In this paper, we study the role of analytics for electricity consumption in smart grids and their possible applications like detecting fraud. Using data-sets of industrial as well as residential consumers, we show how incomplete clustering can help to reduce the search space for these applications. We provide a framework for iterative incomplete clustering and illustrate results in our data-sets. We find, incomplete clustering via correlation coefficients can identify a variety of different households and industries with unique characteristics that are missed with other clustering approaches.