Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Ulf Brefeld,Elisa Fromont,Céline Robardet Conference proceeding

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Sets of Robust Rules, and How to Find Theml important local dependencies in data. The problem is, however, that there are so many of them. Both traditional and state-of-the-art frameworks typically yield millions of rules, rather than identifying a small set of rules that capture the most important dependencies of the data. In this paper, w

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A Framework for Deep Constrained Clustering - Algorithms and Advancesor popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of c

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Unsupervised and Active Learning Using Maximin-Based Anomaly Detectionr One-class Support Vector Machines. One-class Support Vector Machines reduce the computational cost of testing new data by providing sparse solutions. However, all these techniques have relatively high computational requirements for training. Moreover, identifying anomalies based solely on density

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Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisationsensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the ‘crowding problem’ of SNE. Here, we develop an efficient implementation of t-SNE for a t-distribution kernel with an arbitrary degree of freedom ., with . corresponding to SNE and . correspond

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Uncovering Hidden Block Structure for Clusteringtral part of the process is to scale the adjacency matrix into a doubly-stochastic form, which permits detection of the whole matrix block structure with minimal spectral information (theoretically a single pair of singular vectors suffices)..We present the different stages of our method, namely the

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