Privacy‐preserving constrained spectral clustering algorithm for large‐scale data sets

Clustering Algorithm for Arbitrary Data Sets

Clustering analysis is an intrinsic component of numerous applications, including pattern recognition, life sciences, image processing, web data analysis, earth sciences, and climate research. As an example, consider the biology domain. In any living cell that undergoes a biological process, different subsets of its genes are expressed in different stages of the process. To facilitate a deeper ...

A Clustering Algorithm for Logfile Data Sets

Today, vast amounts of system status and health information are stored in logfiles. Therefore, mining patterns from logfiles is an important system management task. This paper presents a novel clustering algorithm for logfile data sets which helps one to detect frequent patterns from logfiles, to build logfile models, and to identify anomalous logfile lines.

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

Constrained spectral embedding for K-way data clustering

Constrained 1-Spectral Clustering

An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering technique which integrates such constraints. Motivated by the recently proposed 1-spectral clustering for the unconstrained problem, our method is based on a tight relaxation of the constrained normalized cut into a continuou...