Special Issue "Recent Developments in Clustering and Classification Methods"
Special Issue Editors
Interests: clustering and classification; mixture models; multivariate dependence models with copula
Interests: biostatistics; categorical data analysis; clustering and classification; computational statistics; mixture models; goodness-of-fit tests; statistical modeling
Special Issue Information
Dear colleagues,
It is our pleasure to announce a Special Issue on “Recent Developments in Clustering and Classification Methods”. This Special Issue aims at providing a focus on state-of-the-art research in the field of statistical models and learning techniques in different areas of unsupervised and semi-supervised classification.
Topics of particular interest may include but are not limited to:
- Development of methodological innovations in all fields of classification and clustering, such as model-based clustering, mixture models for both continuous, discrete, and mixed data in one or two dimensions (co-clustering, biclustering), robust approaches for data classification, and clustering time series, among others;
- New trends in visualization tools;
- Development of new approaches for model selection and goodness-of-fit tests in clustering and classification methods;
- New solutions for dealing with missing data in clustering and classification methods.
We look forward to receiving your submissions.
Sincerely,
Dr. Marta Nai Ruscone
Dr. Daniel Fernández
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- classification and clustering
- graphical tools
- goodness-of-fit test
- mixture models
- model selection
- missing data
- unsupervised and semi-supervised classification
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title:
Spectral Clustering of Mixed-type data
Author:
Cristina Tortora
Abstract:
Cluster analysis seeks to assign objects with similar characteristics into groups called clusters such that objects within a group are similar to each other and dissimilar to objects in other groups. Most popular clustering methods work on either quantitative continuous data or qualitative (categorical) data. Among them, spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and not-convex clusters, it is robust to outliers, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e. data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean based similarity distance used in regular spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios are compared with that of two state of the art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated data sets. The simulated data were design to test the effect of several factors on the clustering performance, specifically we tested the effect of: different number of clusters (2 or 4), the degree of overlap in the variables, the number of continuous-categorical variables, the number of levels in the categorical variable, and whether or not the clusters were balanced (number of points per cluster).
