Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database

On Clustering Binary Data

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. This is the case for market basket datasets where the transactions contain items and for document datasets where the documents contain “bag of words”. The contributio...

Clustering of Sparse Binary Data

Scalable Data Clustering using GPU Clusters

The computational demands of multivariate clustering grow rapidly, and therefore processing large data sets, like those found in flow cytometry data, is very time consuming on a single CPU. Fortunately these techniques lend themselves naturally to large scale parallel processing. To address the computational demands, graphics processing units, specifically NVIDIA’s CUDA framework and Tesla arch...

Clustering Binary Data with Bernoulli Mixture Models

Clustering is an unsupervised learning technique that seeks “natural” groupings in data. One form of data that has not been widely studied in the context of clustering is binary data. A rich statistical framework for clustering binary data is the Bernoulli mixture model for which there exists both Bayesian and non-Bayesian approaches. This paper reviews the development and application of Bernou...

Fuzzy clustering of spatial binary data

An iterative fuzzy clustering method is proposed to partition a set of multivariate binary observation vectors located at neighboring geographic sites. The method described here applies in a binary setup a recently proposed algorithm, called Neighborhood EM, which seeks a a partition that is both well clustered in the feature space and spatially regular [2]. This approach is derived from the EM...