This demo showcases Thoughtland, an end-to-end system that takes training data and a selected machine learning model, produces a cloud of points via crossvalidation to approximate its error function, then uses model-based clustering to identify interesting components of the error function and natural language generation to produce an English text summarizing the error function.

The Nyström sampling provides an efficient approach for large scale clustering problems, by generating a low-rank matrix approximation. However, existing sampling methods are limited by their accuracies and computing times. This paper proposes a scalable Nyström-based clustering algorithm with a new sampling procedure, Minimum Sum of Squared Similarities (MSSS). Here we provide a theoretical an...

In this paper, we present the systems developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2015. The systems propose two different strategies for person discovery in audio through speaker diarization (one based on an online clustering strategy with error correction using OCR information and the other based on agglomerative hierarchical clustering) as ...

◮ It can compute exact and approximate probabilities with error guarantees for the clustering output. State-of-the-art techniques (e.g. UK-means, UKmedoids, MMVar): ◮ do not support the possible worlds semantics, ◮ lack support for correlations and assume probabilistic independence, ◮ use deterministic cluster medoids or expected means, and ◮ can only compute clustering based on expected distan...

materials and methods the segmentation process is evaluated using four different clustering methods with different number of clusters where some dti scalar indices for 10 human brains are processed. results the aim was to produce results with less segmentation error and a lower computational cost while attempting to minimizing boundary overlapping and minimizing the effect of artifacts due to m...

the aim of this work is to use self organizing map (som) for clustering of locomotion kinetic characteristics in normal and parkinson’s disease. the classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. the proposed methodology aims at overcoming the constraints of traditional analysi...

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical signi cance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Du o and Mullainath...

The minimum sum-of-squared error clustering problem is shown to be a concave continuous optimization problem whose every local minimum solution must be integer. We characterize its local minima. A procedure of moving from a fractional solution to a better integer solution is given. Then we adapt Tuy’s convexity cut method to find a global optimum of the minimum sum-of-squared error clustering p...