The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR data is mostly tool-driven, i.e., building on available or newly developed methods. However, these methods, their input requirements, and, importantly, resulting output are frequently difficult to interpret, especially without in-depth data science or statistical training. This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed.This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results, namely pattern screening, meta clustering, surrogate modeling, and curation. These tools can be used at different stages within the analysis. As compared to a standard analysis approach, we demonstrate the ability to condense results and optimize analysis time. In the case of meta clustering, we demonstrate that the number of patient clusters can be reduced from 72 to 3 in one example. In another stratification result, by using surrogate models, we could quickly identify that heart failure patients were stratified if blood sodium measurements were available. As this is a routine measurement performed for all patients with heart failure, this indicated a data bias. By using further cohort and feature curation, these patients and other irrelevant features could be removed to increase the clinical meaningfulness. These examples show the effectiveness of the proposed methods and we hope to encourage further research in this field.

Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same connectivity and allows reduction functions to take into account all the pixels of an image. However, a pooling satisfying such properties does not exist for graphs. Indeed, some methods are based on a vertex selection step which induces an important loss of information. Other methods learn a fuzzy clustering of vertex sets which induces almost complete reduced graphs. We propose to overcome both problems using a new pooling method, named MIVSPool. This method is based on a selection of vertices called surviving vertices using a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets.

Extracting knowledge from unlabeled texts using machine learning algorithms can be complex. Document categorization and information retrieval are two applications that may benefit from unsupervised learning (e.g., text clustering and topic modeling), including exploratory data analysis. However, the unsupervised learning paradigm poses reproducibility issues. The initialization can lead to variability depending on the machine learning algorithm. Furthermore, the distortions can be misleading when regarding cluster geometry. Amongst the causes, the presence of outliers and anomalies can be a determining factor. Despite the relevance of initialization and outlier issues for text clustering and topic modeling, the authors did not find an in-depth analysis of them. This survey provides a systematic literature review (2011-2022) of these subareas and proposes a common terminology since similar procedures have different terms. The authors describe research opportunities, trends, and open issues. The appendices summarize the theoretical background of the text vectorization, the factorization, and the clustering algorithms that are directly or indirectly related to the reviewed works.

This work studies the classical spectral clustering algorithm which embeds the vertices of some graph $G=(V_G, E_G)$ into $\mathbb{R}^k$ using $k$ eigenvectors of some matrix of $G$, and applies $k$-means to partition $V_G$ into $k$ clusters. Our first result is a tighter analysis on the performance of spectral clustering, and explains why it works under some much weaker condition than the ones studied in the literature. For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering. Besides its conceptual and theoretical significance, the practical impact of our work is demonstrated by the empirical analysis on both synthetic and real-world datasets, in which spectral clustering produces comparable or better results with fewer than $k$ eigenvectors.

Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of "Curse of dimensionality" on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the "FlexCWM" R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.

Latent Dirichlet Allocation, or LDA for short, is an unsupervised machine learning algorithm. Similar to the clustering algorithm K-means, LDA will attempt to group words and documents into a predefined number of clusters (i.e. These topics can then be used to organize and search through documents. The most popular methods for estimating the LDA model is Gibbs sampling. Let's walk through one iteration of the algorithm.

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-complicated optimization and intensive computational cost. In this paper, we propose late fusion MVC via alignment maximization to address these issues. To do so, we first reveal the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one. Based on this observation, we propose a simple but effective multi-view algorithm termed LF-MVC-GAM. It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones. Such an alignment is beneficial to integrate partition level information and significantly reduce the computational complexity by sufficiently simplifying the optimization procedure. We then design another variant, LF-MVC-LAM to further improve the clustering performance by preserving the local intrinsic structure among multiple partition spaces. After that, we develop two three-step iterative algorithms to solve the resultant optimization problems with theoretically guaranteed convergence. Further, we provide the generalization error bound analysis of the proposed algorithms. Extensive experiments on eighteen multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The codes of the proposed algorithms are publicly available at https://github.com/wangsiwei2010/latefusionalignment.

This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to explain the decisions of the classification models. The explanations are used to obtain insights into the clustering models. We perform a detailed numerical study to test the proposed approach on multiple datasets, clustering models, and classification models. The analysis of the results shows that the proposed approach can be used to explain time series clustering models, specifically when the underlying classification model is accurate. Lastly, we provide a detailed analysis of the results, discussing how our approach can be used in a real-life scenario.

This volume contains the Late-Breaking Abstracts accepted at Evo* 2022 Conference, held in Madrid (Spain), from 20 to 22 of April. They were also presented as short talks as well as at the conference's poster session. The works present ongoing research and preliminary results investigating on the application of different approaches of Evolutionary Computation and other Nature-Inspired techniques to different problems, most of them real world ones. These are very promising contributions, since they outline some of the incoming advances and applications in the area of nature-inspired methods, mainly Evolutionary Algorithms.

This is evident from popular phenomena such as effects of fake news and online social movements. However the the data obtained from social media presents itself with large volume and velocity, accompanied by significant amount of irrelevant data pertaining to general discussions, personal messages and spam. Social media has been shown to be effective for detecting, forecasting and tracking real world events. The ability to detect real world events is crucial and has applications in disease surveillance, commerce, governance and other areas. Thus extraction of useful information and modelling the characteristics of social media to detect real world events is an important problem. 2 RESEARCH PROBLEM To outline the research problem we need to define events, which has multiple interpretations.