1 citations · 1 across the 6 of their papers we have counts for
6 papers
Bayesian Nonparametric Clustering to Support Medical Decision-Making: A Variational Inference Approach
Inga Huld Ármann, Ioanna Papatsouma, Marina Evangelou
Medical decision-making increasingly requires rapid and reliable assignment of patients to disease subtypes, as many diseases are no longer treated as single entities. For example,…
Sparse clustering via the Deterministic Information Bottleneck algorithm
Efthymios Costa, Ioanna Papatsouma, Angelos Markos
Cluster analysis relates to the task of assigning objects into groups which ideally present some desirable characteristics. When a cluster structure is confined to a subset of the…
A novel framework for quantifying nominal outlyingness
Efthymios Costa, Ioanna Papatsouma
Outlier detection is an important data mining tool that becomes particularly challenging when dealing with nominal data. First and foremost, flagging observations as outlying requi…
A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos
In this paper, we present an information-theoretic method for clustering mixed-type data, that is, data consisting of both continuous and categorical variables. The proposed approa…
Outlier detection for mixed-type data: A novel approach
Efthymios Costa, Ioanna Papatsouma
Outlier detection can serve as an extremely important tool for researchers from a wide range of fields. From the sectors of banking and marketing to the social sciences and healthc…
Benchmarking distance-based partitioning methods for mixed-type data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos
Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges…