An Adaptive Robust Semi-supervised Clustering Framework Using Weighted Consensus of Random k-Means Ensemble

Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. In this paper, we propose a novel semi-supervised consensus clustering algorithm based on multi-ant colonies. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the multi-ant colonies ensemble. Experimental results demonstrate the effectiveness of the proposed method.

Semi-supervised clustering leverages side information such as pairwise constraints to guide clustering procedures. Despite promising progress, existing semi-supervised clustering approaches overlook the condition of side information being generated sequentially, which is a natural setting arising in numerous real-world applications such as social network and e-commerce system analysis. Given emerged new constraints, classical semi-supervised clustering algorithms need to re-optimize their objectives over all data samples and constraints in availability, which prevents them from efficiently updating the obtained data partitions. To address this challenge, we propose an efficient dynamic semi-supervised clustering framework that casts the clustering problem into a search problem over a feasible convex set, i.e., a convex hull with its extreme points being an ensemble of m data partitions. According to the principle of ensemble clustering, the optimal partition lies in the convex hull, and can thus be uniquely represented by an m-dimensional probability simplex vector. As such, the dynamic semi-supervised clustering problem is simplified to the problem of updating a probability simplex vector subject to the newly received pairwise constraints. We then develop a computationally efficient updating procedure to update the probability simplex vector in O(m2) time, irrespective of the data size n. Our empirical studies on several real-world benchmark datasets show that the proposed algorithm outperforms the state-of-the-art semi-supervised clustering algorithms with visible performance gain and significantly reduced running time.

This paper presents semi-supervised agglomerative hierarchical clustering algorithm using clusterwise tolerance based pairwise constraints. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering properties. From that sense, we will propose another way named clusterwise tolerance based pairwise constraints to handle must-link and cannot-link constraints inL2-space. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithm based on it. We will, moreover, show the effectiveness of the proposed method through numerical examples.

In this paper, a scheme of recognizing hematopoietic tumor patients is presented, using self-organizing maps constructed by fast block-matching-based learning. This fast learning is referred to as T-BMSOM leaning. To classify the patients, screening data of examinees are presented to a constructed map. In T-BMSOM learning, a set of neurons arranged in square is regarded as a block, and one of the blocks is chosen as a winner per the presented data. It is assumed that members of a training data set to construct the map never change in static environments, whereas the data set is suddenly updated during learning in dynamic environments. While adopting the concept of blocks makes it possible to construct well-organized maps in dynamic environments, it lengthens the time for learning. To overcome this issue, T-BMSOM learning is based on a decision-tree-like winner search and a batch process. The screening data of an examinee frequently lacks several of the item values, and hence the data is presented to the map after averages of non-missing item values substitute for items with no values. The class of the data to be classified is basically judged by observing the label of a winner block. Simulation results establish that the proposed scheme achieves high accuracy of correctly recognizing the data of hematopoietic tumor patients, even if training the map is conducted in a dynamic environment.

During the past few years, semi-supervised clustering has emerged as a new interesting direction in machine learning research. In a semi-supervised clustering algorithm, the clustering results can be significantly improved by using side information, which is available or collected from users. There are two main kinds of side information that can be learned in semi-supervised clustering algorithms: the class labels - called seeds or the pairwise constraints. The first semi-supervised clustering was introduced in 2000, and since that, many algorithms have been presented in literature. However, it is not easy to use both types of side information in the same algorithm. To address the problem, this paper proposes a semi-supervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we introduces a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. In order to verify effectiveness of the proposed algorithm, we conducted a series of experiments not only on real data sets from UCI, but also on a document data set applied in an Information Extraction of Vietnamese documents. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms.

Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is based on the HCAC algorithm, an state-of-the-art algorithm for hierarchical semi-supervised clustering that uses an active learning approach for inserting cluster-level constraints. These constraints are presented to a variation of ITML (Information-theoretic Metric Learning) algorithm to learn a Mahalanobis-like distance function. We compared HCAC-ML with other semi-supervised clustering algorithms in 26 different datasets. Results indicate that HCAC-ML outperforms other algorithms in most of the scenarios, but specially when the number of constraints is small. This makes HCAC-ML useful in practical applications.

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

Semi-supervised clustering methods are applied in various fields. However, these methods have the limitations when the size of data is large. Thus, distributed clustering models are proposed. By separating data set into some parts, distributed models overcome the challenges of big size dataset. In this paper, a novel model, based on semi-supervised fuzzy clustering and distributed mechanism, is proposed. In our research, the method to define distributed additional information is introduced. This additional information is used to implement distributed semi-supervised fuzzy clustering model on three datasets. The main contribution in this paper is the proposal of distributed semi-supervised fuzzy clustering algorithm (denoted as DSSFCM) that performs on Master – Slave model on homogeneous datasets.

An Improved Semi-Supervised Clustering Algorithm for Multi-Density Datasets with Fewer Constraints

Semi-supervised clustering takes advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper presents a novel semi-supervised clustering method based on one-class support vector machine, which applies a novel decision rule to assigning the class label to one data point. To investigate the effectiveness of our approach, experiments are done on one artificial data set and two real datasets. Experimental results show that the proposed method can improve the clustering performance significantly compared to other semi-supervised clustering algorithms when using a very small amount of seeds.

ObjectiveCluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. MethodsWe designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. ResultsAfter applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. ConclusionCompared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. SignificanceOur study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student’s needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

In this paper, we address the autonomous control of a 3D snake-like robot through the use of reinforcement learning, and we apply it in a dynamic environment. In general, snake-like robots have high mobility that is realized by many degrees of freedom, and they can move over dynamically shifting environments such as rubble. However, this freedom and flexibility leads to a state explosion problem, and the complexity of the dynamic environment leads to incomplete learning by the robot. To solve these problems, we focus on the properties of the actual operating environment and the dynamics of a mechanical body. We design the body of the robot so that it can abstract small, but necessary state-action space by utilizing these properties, and we make it possible to apply reinforcement learning. To demonstrate the effectiveness of the proposed snake-like robot, we conduct experiments; from the experimental results we conclude that learning is completed within a reasonable time, and that effective behaviors for the robot to adapt itself to an unknown 3D dynamic environment were realized.

Clustering is often considered as an unsupervised data analysis method, but making full use of the prior information in the process of clustering will significantly improve the performance of the clustering algorithm. Spectral clustering algorithm can well use the prior pairwise constraint information to cluster and has become a new hot spot of machine learning research in recent years. In this paper, we propose an effective clustering algorithm, called a semi-supervised spectral clustering algorithm based on pairwise constraints, in which the similarity matrix of data points is adjusted and optimized by pairwise constraints. The experiments on real-world data sets demonstrate the effectiveness of this algorithm.

The intrinsic gradient in organisms governs long-term information processing and continual learning in dynamic environments. Reinforcement learning (RL) aims to emulate such temporal regulation to enhance learning efficiency and adaptability. However, the absence of intrinsic gradient construction in existing memristive devices leads to stochastic and abrupt state changes, disrupting the generation of temporally correlated internal states critical for continual RL. In this work, a second-order memristor incorporating a stable intrinsic oxygen gradient was designed via a molecular-coordinated layer, which enables a prolonged dynamic barrier evolution (>102 s). This slow dynamic response facilitates balanced oxygen ion migration and diffusion under unipolar spike stimulation, resulting in a significant conductance modulation (ΔG = -98.1%). These temporally adaptive conductance states were quantitatively mapped to learning rates in the RL algorithm, allowing the learning task timescale to co-evolve with device dynamics. Compared with conventional strategies, intrinsic-gradient-driven modulation reduced training iterations by 68.75% and 35.65% in static and dynamic environments, respectively. These findings underscore the potential of slow-dynamic second-order memristors as physically grounded time-adaptive units bridging device dynamics and algorithmic learning in neuromorphic systems.