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Found 15 papers, 0 papers with code
An Efficient Shapley Value Computation for the Naive Bayes Classifier
In the case of the naive Bayes classifier, and to our knowledge, there is no ``analytical" formulation of Shapley values.
Two-level histograms for dealing with outliers and heavy tail distributions
Histograms are among the most popular methods used in exploratory analysis to summarize univariate distributions.
Fast and fully-automated histograms for large-scale data sets
G-Enum histograms are a new fast and fully automated method for irregular histogram construction.
Co-clustering based exploratory analysis of mixed-type data tables
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks.
Model Based Co-clustering of Mixed Numerical and Binary Data
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix.
Interpretable Feature Construction for Time Series Extrinsic Regression
Supervised learning of time series data has been extensively studied for the case of a categorical target variable.
Analysis of the AutoML Challenge Series 2015–2018
The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn.
Ranked #1 on
AutoML
on Chalearn-AutoML-1
Un modèle Bayésien de co-clustering de données mixtes
The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function.
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach
This paper introduces a novel technique to track structures in time varying graphs.
Co-Clustering Network-Constrained Trajectory Data
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities.
A Study of the Spatio-Temporal Correlations in Mobile Calls Networks
In this article, we introduce a practical analysis of a large database from a telecommunication operator.
Universal Approximation of Edge Density in Large Graphs
In this paper, we present a novel way to summarize the structure of large graphs, based on non-parametric estimation of edge density in directed multigraphs.
Cats & Co: Categorical Time Series Coclustering
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models.
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models
Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination, date and time of calls.
Nonparametric Hierarchical Clustering of Functional Data
In this paper, we deal with the problem of curves clustering.
