Spectral Clustering of Mixed-Type Data

Integrative Parameter-Free Clustering of Data with Mixed Type Attributes

Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing solutions suffer from one or both of the following drawbacks: Either they require input parameters which are difficult to estimate, or/and they do not adequately support mixed type attribute...

Clustering of samples and variables with mixed-type data

Analysis of data measured on different scales is a relevant challenge. Biomedical studies often focus on high-throughput datasets of, e.g., quantitative measurements. However, the need for integration of other features possibly measured on different scales, e.g. clinical or cytogenetic factors, becomes increasingly important. The analysis results (e.g. a selection of relevant genes) are then vi...

CluMix: Clustering and Visualization of Mixed-Type Data

In real data situations various factors of interest are measured on different scales, e.g. quantitative gene expression values and categorical clinical features like gender, disease stage etc. In many cases (pre-selected) gene expression data are visualized in heatmaps, while further patient characteristics are only added ”informatively” on top. This can be visually quite confusing in case ther...

MK-Prototypes: A Novel Algorithm for Clustering Mixed Type Data

Spectral Clustering of Data Streams

The data is modeled as an m × n matrix P n points, each m-dimensional that is arriving as a stream, a point at a time. Our algorithm has two components stream framework StrFr and spectral clustering algorithm SpCl. SpCl works on an adjacency matrix representation of the points by recursively producing cuts on the graph thus defined. StrFr takes in the stream in chunks of size n1, converts a chu...