Recent developments in the frequent pattern mining framework uses additional measures of
interest to reduce the set of discovered patterns. We introduce a rigorous and efficient …

This paper presents a framework for exact discovery of the top-k sequential patterns under
Leverage. It combines (1) a novel definition of the expected support for a sequential pattern …

The identification of significant patterns, defined as patterns whose frequency significantly
deviates from what is expected under a suitable null model of the data, is a key data mining …

We investigate the problem of counting the number of frequent (item) sets—a problem
known to be intractable in terms of an exact polynomial time computation. In this paper, we …

Supervised local pattern discovery aims to find subsets of a database with a high statistical
unusualness in the distribution of a target attribute. Local pattern discovery is often used to …

Here, we present a novel algorithm for frequent itemset mining in streaming data (FIM-SD).
For the past decade, various FIM-SD methods in one-pass approximation settings that allow …

These years have seen the advances of compressive sensing (CS), but efficient coding of
sensed measurements is still an issue. In this paper, we propose an image coding system …

We consider the problem of mining strongly closed itemsets from transactional data streams.
Compactness and stability against changes in the input are two characteristic features of this …

In recent years, coordinate-based meta-analyses have become a powerful and widely used
tool to study co-activity across neuroimaging experiments, a development that was …

Reducing leakage in memories is critical to reduce static power consumption in nanometric
technologies. A wide-spread technique for reducing the leakage consists of lowering the …