Semi-supervised Clustering Algorithm Research Articles

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist. Multiple semi-supervised machine learning clustering algorithms were applied to 111 normal adolescent sagittal spines. Sagittal parameters for resultant clusters were determined. Machine learning analysis found that the spines did cluster into distinct groups with an optimal number of clusters ranging from 3 to 5. We performed an analysis on both 3 and 5-cluster groups. The 3-cluster groups analysis found good consistency between methods with 96 of 111, while the analysis of 5-cluster groups found consistency with 105 of 111 spines. When assessing for differences in sagittal parameters between the groups for both analyses, there were differences in T4-12 TK, L1-S1 LL, SS, SVA, PI-LL mismatch, and TPA. However, the only parameter that was statistically different for all groups was SVA. Based on machine learning, the adolescent sagittal spine alignments do cluster into distinct groups. While there were distinguishing features with TK and LL, the most important parameter distinguishing these groups was SVA. Further studies may help to understand these findings in relation to spinal deformities.

Study on the Design Parameters of Landscape Elements of Urban Tunnel Body Segments in China Based on Biomass Perception

To provide a comprehensive analysis of how an intricate landscape environment comprising various landscape elements influences driving safety and comfort. Beginning with the individual design of the tunnel body section, which includes the roof, sidewalls, road surface, and sidewalk facade markings, this paper references the driver’s biomass index to identify design alterations that satisfy basic safety requirements through simulated driving experiments. Subsequently, employing orthogonal experimental design and semi-supervised clustering algorithms, we ascertain the optimal combination of the four main landscape elements of the tunnel body section that align with both safety and comfort objectives. The experimental findings demonstrate that a safe and comfortable landscape design for urban tunnel sections does not entail a single optimal design; rather, any landscape design conforming to the criterion of “patterned landscapes are set on the side walls and roofs of the tunnel instead of the surface of the road” is the optimal design.

Semi-supervised clustering guided by pairwise constraints and local density structures

Clustering based on local density peaks and graph cut (LDP-SC) is one of the state-of-the-art algorithms in unsupervised clustering, which first divides the data set to be multiple local trees, and then aggregates these local trees to obtain the final clustering result. However, for complex data sets, there might exist data points from different classes in the same local tree. In this article, we use pairwise constraint information to resolve this issue and propose a semi-supervised local density peaks and graph cut based clustering algorithm (SLDPC). In particular, SLDPC proposes intra-cluster conflict resolution and inter-cluster conflict resolution steps to split the local trees which are inconsistent with the provided pairwise constraint information. Theoretically, we show that the two steps will finish in a finite number of operations and the split local trees will be consistent with the pairwise constraint information. Subsequently, root node redirection and noise filtering steps are designed to avoid the local trees becoming too fragmented. Finally, we exploit the E2CP algorithm to further improve the similarity matrix between local trees using the pairwise constraint information, and the spectral clustering algorithm is adopted to obtain the clustering result. Experiments on multiple widely used synthetic and real-world data sets show that SLDPC is superior to LDP-SC and several other semi-supervised prominent clustering algorithms for most of the cases.

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

<p>In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a Semi-supervised Spectral Clustering algorithm based on shared nearest neighbor. The proposed algorithm combines the idea of semi-supervised clustering, adding Shared Nearest Neighbor information to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.</p>

Electrical Sensor Calibration by Fuzzy Clustering with Mandatory Constraint.

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.

Enhanced Detection of Social Bots on Online Platforms using SemiSupervised K-Means Clustering

Social bots are semi-automatic or automatic computer applications that express human performance in OSN. Social bots are the primary tools utilized by hackers to invade OSNs. The current use of Social bots in communication and voting operations has been highlighted. Twitter and Tumblr have been efficiently used to share information about public sentiment. The developing connection on the Internet has started up avenues for improved cybersecurity threats and perpetuation of an extensive array of cybercrimes occurring in significant financial needs and user data privacy violations. One of the most advanced but critical extensions to the public of malicious software is the bot malware, commonly referred to as botnets. The most current presentation techniques of malicious social bots examine the quantitative characteristics of their behavior. This paper proposed a novel approach to identifying malicious social bots, including feature determination based on the development probability of clickstream progressions and the Semi-supervised K-Means Clustering algorithm for detecting social bots. The proposed method explains the transition probability of user behavior clickstreams and reflects the time feature. The proposed Semi-supervised K-Means Clustering (SSKMC) algorithm, compared with the traditional detection method based on the quantitative function, improves accuracy by 15% on average. The proposed SSKMC Algorithm can efficiently detect malicious accounts on social bots.

Online semi-supervised active learning ensemble classification for evolving imbalanced data streams

Concept drift is a core challenge in classification tasks of data streams. Although many drift adaptation methods have been presented, most of them assume that labels of all data are available, which is impractical in many real-world applications. Additionally, the absence of label makes the imbalance ratio of an imbalanced data stream difficultly being obtained in time, providing the inaccurate guidance for resampling and causing poor generalization. To tackle the joint challenges, an online semi-supervised active learning method is proposed to classifier imbalanced data streams with concept drift. A newly-arrived data is first added to the sliding window, and then assigned a pseudo label in terms of its nearest cluster. Meanwhile, semi-supervised clustering algorithm offers its predicted label. Based on the above two predictive labels, cluster-based query strategy provides the criteria for the evaluation and selection of representative instances. More especially, the uncertainty and importance of instances are defined to synthetically evaluate its representativeness. After obtaining true labels of typical ones, ensemble classifier is updated by all instances in current sliding window. Experimental results on 13 synthetic and real data streams indicate that the proposed method outperforms six comparative methods on both G-mean and Recall under various labeling budgets.

A game model for semi-supervised subspace clustering with dynamic affinity and label learning

With the aid of partial supervised information, semi-supervised subspace clustering methods aim to obtain affinity matrices directly derived from raw data, and then those affinity matrices are utilized to get assignment matrices. These affinity matrices are susceptible to disturbances such as noise and outliers, which could significantly impact their quality. To mitigate this, it becomes essential to dynamically update the affinity matrix using the clustering result, reducing the high dependency on raw data. This paper presents a Game Model for Semi-supervised Subspace Clustering (GMSSC). The first submodule of GMSSC utilizes a Graph Convolutional Network to learn a mapping from raw data to the assignment matrix with the assistance of the affinity matrix. The second submodule constructs a semi-supervised self-expressive model to learn a discriminative affinity matrix. By integrating these submodules into a game model, GMSSC achieves a Nash Equilibrium during adversarial training, resulting in stable and robust affinity and assignment matrices. Additionally, we propose an iterative algorithm based on data-driven and model-driven approaches to solve the model. Experimental results on four open real-world datasets demonstrate that the proposed method not only achieves elegant results but also outperforms state-of-the-art semi-supervised subspace clustering algorithms.

A new semi-supervised clustering algorithm for probability density functions and applications

A new semi-supervised clustering algorithm for probability density functions and applications

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = –18.3, 95% CI (–34.3 to –2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

An end-to-end Graph Convolutional Network for Semi-supervised Subspace Clustering via label self-expressiveness

Semi-supervised clustering provides accurate assignments by leveraging a limited amount of supervisory information. There are two main groups of conventional semi-supervised subspace clustering algorithms: Pairwise Constraints (PC)-based methods and Gaussian Fields and Harmonic Function (GFHF)-based methods. Nevertheless, PC-based methods are limited because of their reliance on weak supervisory information. GFHF-based methods, on the other hand, focus on the local relationship between the affinity matrix and the assignment matrix, which restricts their clustering efficacy. Recently, graph convolutional network (GCN) has been employed to effectively transmit the local graph topology of the raw data into the feature domain, which motivates the proposed GCN model for Semi-supervised Subspace Clustering via label self-expressiveness (GSSC). Conventional semi-supervised clustering has been enhanced by incorporating a novel concept known as label self-expressiveness, which serves as a guiding factor for the clustering process. Furthermore, GSSC considers the global geometric structure of the assignment matrix and generates a discriminable assignment matrix using an end-to-end mechanism. Additionally, a well-designed loss function is introduced to train the novel GSSC. The experimental results conducted on four benchmark datasets demonstrate that the proposed GSSC algorithm exhibits superior performance compared to existing semi-supervised clustering algorithms.

Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.

Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of memberships. The semi-supervised possibilistic c-means clustering (SSPCM) algorithm improves the clustering effect on datasets with imbalanced sizes by introducing a small amount of label information. However, the traditional semi-supervised algorithm still faces the problem of low utilization of supervision information for datasets with large differences in sample sizes. Moreover, the Euclidean distance, which treats features equally, cannot handle feature-imbalanced data. Therefore, this paper proposes a semi-supervised possibilistic c-means clustering algorithm based on feature weights (FW-SSPCM) by introducing the ideas of supervised centers. First, the algorithm introduces the supervised center into the objective function of the SSPCM to improve the utilization rate of supervision information and thus guide the center iteration of small clusters. Second, the feature weighting strategy is introduced in the objective function to adaptively assign feature weights according to the importance of different features in different clusters, thus improving the adaptability of the algorithm to feature-imbalanced datasets. In addition, to improve the robustness of the antinoise effect and retain additional image details, a new image segmentation algorithm based on FW-SSPCM and local information (LFW-SSPCM) is proposed by introducing local spatial information obtained by bilateral filtering. Finally, through clustering experiments on synthetic data, UCI datasets and on color images characteristic of multiple features, including imbalanced sizes, imbalanced features and strong noise injection, the clustering performances of the proposed FW-SSPCM and LFW-SSPCM proposed in this paper are significantly better than those of several related clustering algorithms.

Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual Analytics

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the analyst wishes to find within the data. However, determining the precise intent of the analyst is a challenge. When an analyst interacts with a projection, the inherent ambiguity of interactions can lead to a variety of possible interpretations that the system can infer. Previous work has demonstrated the utility of clusters as an interaction target to address this “With Respect to What” problem in dimension-reduced projections. However, the introduction of clusters introduces interaction inference challenges as well. In this work, we discuss the interaction space for the simultaneous use of semi-supervised dimension reduction and clustering algorithms. We introduce a novel pipeline representation to disambiguate between interactions on observations and clusters, as well as which underlying model is responding to those analyst interactions. We use a prototype visual analytics tool to demonstrate the effects of these ambiguous interactions, their properties, and the insights that an analyst can glean from each.

Semi-Supervised Clustering Algorithms Through Active Constraints

Semi-Supervised Clustering Algorithms Through Active Constraints

Interclass Balance Factor-Based Membership Fusion Semi-Supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

Interclass Balance Factor-Based Membership Fusion Semi-Supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications.

In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters. The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.

Active Semi-Supervised Clustering Algorithm for Multi-Density Datasets

Active Semi-Supervised Clustering Algorithm for Multi-Density Datasets

A cooperative spectrum sensing method based on semi-supervised clustering with variational mode decomposition and information geometry

In practice, obtaining a large number of sample labels for the supervised learning-based cooperative spectrum sensing (CSS) method is challenging. Additionally, the CSS method based on information geometry (IG) and clustering algorithm exhibited low detection performance in low signal-to-noise ratio (SNR) environment. This paper proposes a cooperative spectrum sensing method based on variational mode decomposition and IG with semi-supervised clustering. First, the secondary users (SUs) reconstruct the received signals using variational mode decomposition. Next, the fusion center combines all the reconstructed signals into a single matrix and calculates its corresponding covariance matrix. Finally, a binary classifier is obtained via training offline on the matrix manifold space using a semi-supervised clustering algorithm based on IG. The simulation results show that increasing the number of SUs and sampling points can improve the CSS performance of the proposed algorithm. Furthermore, the proposed algorithm is compared with other CSS algorithms, demonstrating its superior spectrum sensing performance regardless of whether the SUs are in the same very low SNR environment.

Semi-supervised fuzzy clustering algorithm based on prior membership degree matrix with expert preference

Existing pre-processing methods for the prior membership degree matrix suffer from the following issues: (1) The labeling constraints for prior membership degree matrix have an effect on the expert’s judgment on the prior membership degree, which easily causes the distortion problem of the prior membership degree labeling information; (2) There exists the problem of inconsistency between the filling information and the labeling information in the prior membership degree matrix to be filled in the missing values with zeros. To address these problems, we propose an unconstrained labeling idea for the prior membership degree matrix and the corresponding pre-processing method for the missing values by introducing the statistical characteristics of extreme value distribution and simultaneously apply it to the semi-supervised fuzzy clustering algorithm. More specifically, we focus on learning an expert preference value from the prior membership degree matrix and filling in the missing values with the expert preference value. Thus, we propose an unconstrained pre-processing method for the prior membership degree matrix by filling in missing values with an expert preference to keep the filling information consistent with the labeling information in the prior membership degree matrix as much as possible. In addition, we design a semi-supervised fuzzy clustering algorithm based on an unconstrained prior membership degree matrix with expert preference (SFCM-EP) by introducing the K-L divergence to improve the applicability, utility and running performance of semi-supervised fuzzy clustering algorithm. Our experimental results on the simulation dataset and the UCI datasets show the feasibility and effectiveness of the proposed pre-processing method of the prior membership degree matrix with encouraging results.