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- Andrea Pietracaprina, Dario Zandolin
- FIMI
- 2003

We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs a Patricia trie to represent the dataset, which is space efficient for both dense and sparse datasets, whereas alternative representations were adopted by previous algorithms for… (More)

A quantitative comparison of the BSP and LogP models for parallel computation is developed. Very efficient cross simulations between the two models are derived, showing their substantial equivalence for algorithmic design guided by asymptotic analysis. It is also shown that the two models can be implemented with similar performance on most point-to-point… (More)

Motivated by the growing interest in mobile systems, we study the dynamics of information dissemination between agents moving independently on a plane. Formally, we consider <i>k</i> mobile agents performing independent random walks on an <i>n</i>-node grid. At time 0, each agent is located at a random node of the grid and one agent has a rumor. The spread… (More)

- Andrea Pietracaprina, Fabio Vandin
- Discovery Science
- 2007

In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support threshold is chosen as the maximum value sufficient to guarantee that the itemsets returned in output be at least K. We discuss the effectiveness of parameter K in controlling the… (More)

A quantitative comparison of the BSP and LogP models of parallel computation is developed. We concentrate on a variant of LogP that disallows the so-called stalling behavior, although issues surrounding the stalling phenomenon are also explored. Very eecient cross simulations between the two models are derived, showing their substantial equivalence for… (More)

We develop, analyze and experiment with a new tool, called madmx, which extracts frequent motifs, possibly including don’t care characters, from biological sequences. We introduce density, a simple and flexible measure for bounding the number of don’t cares in a motif, defined as the ratio of solid (i.e., different from don’t care) characters to the total… (More)

- Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin
- Data Mining and Knowledge Discovery
- 2010

We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very frequent (resp., very infrequent) itemsets, together with an estimate of these itemsets’ frequencies with a… (More)

- Roberto Grossi, Andrea Pietracaprina, Nadia Pisanti, Geppino Pucci, Eli Upfal, Fabio Vandin
- Journal of Computational Biology
- 2011

We develop, analyze, and experiment with a new tool, called MADMX, which extracts frequent motifs from biological sequences. We introduce the notion of density to single out the "significant" motifs. The density is a simple and flexible measure for bounding the number of don't cares in a motif, defined as the fraction of solid (i.e., different from don't… (More)

- Roberto Grossi, Andrea Pietracaprina, Geppino Pucci
- SWAT
- 1998

This paper studies a system of m robots operating in a set of n work locations connected by aisles in a pn pn grid, where m n. From time to time the robots need to move along the aisles, in order to visit disjoint sets of locations. The movement of the robots must comply with the following constraints: (1) no two robots can collide at a grid node or… (More)