Although clustering under constraints is a current research topic, a hierarchical setting, in which a hierarchy of clusters is the goal, is usually not considered. This paper trie...
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
With the advent of high throughput technologies, feature selection has become increasingly important in a wide range of scientific disciplines. We propose a new feature selection ...
Currently, many privacy-preserving data mining (PPDM) algorithms assume the semi-honest model and/or malicious model of multi-party interaction. However, both models are far from ...
Understanding the general properties of real social networks has gained much attention due to the proliferation of networked data. The nodes in the network are the individuals and...
In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected ed...
Bei Wang, Jeff M. Phillips, Robert Schreiber, Denn...
The rigid all-versus-all adjacency required by a maximal biclique for its two vertex sets is extremely vulnerable to missing data. In the past, several types of quasi-bicliques ha...
Typically, sequence signatures, such as motifs and domains, are assumed to be localized in one region of a sequence or are derived as combinations of the former. We generalize the...
Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the t...
In this paper, we propose a new approach to anomaly detection by looking at the latent variable space to make the first step toward latent anomaly detection. Most conventional app...