Abstract--Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Nonnegative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multidimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency. This can be (1) multi-view data where samples For instance, in Figure 1.a, three intra-type relationship are represented by multiple views; or (2) multi-type matrices will store intra-similarities between Webpages, relational data (MTRD) where samples are represented by between Terms and between Hyperlinks, and three interrelationships different data types and their inherent relationships.

Deep learning has been widely used for supervised learning and classification/regression problems. Recently, a novel area of research has applied this paradigm to unsupervised tasks; indeed, a gradient-based approach extracts, efficiently and autonomously, the relevant features for handling input data. However, state-of-the-art techniques focus mostly on algorithmic efficiency and accuracy rather than mimic the input manifold. On the contrary, competitive learning is a powerful tool for replicating the input distribution topology. This paper introduces a novel perspective in this area by combining these two techniques: unsupervised gradient-based and competitive learning. The theory is based on the intuition that neural networks are able to learn topological structures by working directly on the transpose of the input matrix. At this purpose, the vanilla competitive layer and its dual are presented. The former is just an adaptation of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix. Their equivalence is extensively proven both theoretically and experimentally. However, the dual layer is better suited for handling very high-dimensional datasets. The proposed approach has a great potential as it can be generalized to a vast selection of topological learning tasks, such as non-stationary and hierarchical clustering; furthermore, it can also be integrated within more complex architectures such as autoencoders and generative adversarial networks.

Clustering of data into groups of relatively similar observations is one of the canonical tasks in unsupervised learning. With an increasing focus in recent years on very richly parameterized models, there has been a corresponding emphasis in the literature on complex clustering algorithms. A popular theme has been on clustering on the latent variable level, while allowing estimation of both the clustering structure and a complex nonlinear mapping from the latent to observed data level. Such methods are appealing in being able to realistically generate data that are indistinguishable from the observed data, while clustering observations in a lower-dimensional space. A particularly popular strategy is to develop clustering algorithms based on variational autoencoders (VAEs). For example, instead of drawing the latent variables in a VAE from standard Gaussian distributions, one can use a mixture of Gaussians for model-based clustering (Dilokthanakul et al., 2016; Lim et al., 2020; Yang et al., 2019). The problem with this family of methods is that, with a rich enough deep neural network, VAEs can accurately approximate any data generating distribution regardless of the continuous density placed on the latent variables. If one uses a richer family of densities, such as a mixture model, then one can potentially approximate the data distribution using a simpler neural network structure. However, the inferred clusters are not reliable due to problems of non-identifiability.

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.

The increased computerization in recent years has resulted in the production of a variety of different software, however measures need to be taken to ensure that the produced software isn't defective. Many researchers have worked in this area and have developed different Machine Learning-based approaches that predict whether the software is defective or not. This issue can't be resolved simply by using different conventional classifiers because the dataset is highly imbalanced i.e the number of defective samples detected is extremely less as compared to the number of non-defective samples. Therefore, to address this issue, certain sophisticated methods are required. The different methods developed by the researchers can be broadly classified into Resampling based methods, Cost-sensitive learning-based methods, and Ensemble Learning. Among these methods. This report analyses the performance of the Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et.al. against several classifiers such as Logistic Regression, Support Vector Machine, Random Forest, and Na\"ive Bayes after oversampling the data. OS-ELM trains faster than conventional deep neural networks and it always converges to the globally optimal solution. A comparison is performed on the original dataset as well as the over-sampled data set. The oversampling technique used is Cluster-based Over-Sampling with Noise Filtering. This technique is better than several state-of-the-art techniques for oversampling. The analysis is carried out on 3 projects KC1, PC4 and PC3 carried out by the NASA group. The metrics used for measurement are recall and balanced accuracy. The results are higher for OS-ELM as compared to other classifiers in both scenarios.

Click to learn more about author Kartik Patel. This article provides a brief explanation of the KMeans Clustering algorithm. The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. KMeans Clustering is a grouping of similar things or data. For example, objects within group 1 (cluster 1) shown in image below should be as similar as possible.

With the recent outbreak of COVID-19, creating a means to stop it's spread and eventually develop a vaccine are the most important and challenging tasks that the scientific community is facing right now. The first step towards these goals is to correctly identify a patient that is infected with the virus. Our group applied an unsupervised machine learning technique to identify COVID-19 cases. This is an important topic as COVID-19 is a novel disease currently being studied in detail and our methodology has the potential to reveal important differences between it and other viral pneumonia. This could then, in turn, enable doctors to more confidently help each patient. Our experiments utilize Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and the recently developed Robust Continuous Clustering algorithm (RCC). We display the performance of RCC in identifying COVID-19 patients and its ability to compete with other unsupervised algorithms, namely K-Means++ (KM++). Using a COVID-19 Radiography dataset, we found that RCC outperformed KM++; we used the Adjusted Mutual Information Score (AMI) in order to measure the effectiveness of both algorithms. The AMI for the two and three class cases of KM++ were 0.0250 and 0.054, respectively. In comparison, RCC scored 0.5044 in the two class case and 0.267 in the three class case, clearly showing RCC as the superior algorithm. This not only opens new possible applications of RCC, but it could potentially aid in the creation of a new tool for COVID-19 identification.

The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we demonstrate that, in the context of internal/relative clustering validation, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a computationally much more efficient and practical algorithmic procedure. Our theoretical findings are supported by experimental results.

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous graph neural networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world graph data. In this paper, we present a novel cluster-aware graph neural network (CAGNN) model for unsupervised graph representation learning using self-supervised techniques. In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. We further refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over existing baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.

One of the most popular Machine Learning algorithms is K-means clustering. It is an unsupervised learning algorithm, meaning that it is used for unlabeled datasets. Imagine that you have several points spread over an n-dimensional space. In order to categorize this data on the basis of their similarity, you will use the K-means clustering algorithm. In this article, we will go through this algorithm in detail.