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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)

- Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin
- Data Min. Knowl. Discov.
- 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)

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)

As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications. In this work, we address significance in the context of frequent itemset mining. Specifically, we develop a novel methodology… (More)

- Fabrizio Luccio, Andrea Pietracaprina, Geppino Pucci
- Algorithmica
- 1990

A deterministic scheme for the simulation of (n, m)-PRAM computation is devised. Each PRAM step is simulated on a bounded degree network consisting of a mesh-of-trees (MT) of side n. The memory is subdivided in n modules, each local to a PRAM processor. The roots of the MT contain these processors and the memory modules, while the other O(n 2) nodes have… (More)

- Andrea Pietracaprina, Geppino Pucci, Jop F. Sibeyn
- SPAA
- 1994

We present a constructive deterministic simulation of a PRAM with <italic>n</italic> processors and <italic>m</italic> = <italic>n</italic> <supscrpt>α</supscrpt> shared variables, 1 < α ≤ 2, on an <italic>n</italic>-node mesh-connected computer where each node hosts a processor and a memory module. At the core of the simulation is a… (More)

This work explores fundamental modeling and algorithmic issues arising in the well-established MapReduce framework. First, we formally specify a computational model for MapReduce which captures the functional flavor of the paradigm by allowing for a flexible use of parallelism. Indeed, the model diverges from a traditional processor-centric view by… (More)

- Gianfranco Bilardi, Carlo Fantozzi, Andrea Pietracaprina, Geppino Pucci
- International Conference on Computational Science
- 2001

This paper surveys and places into perspective a number of results concerning the D-BSP (Decomposable Bulk Synchronous Parallel) model of computation , a variant of the popular BSP model proposed by Valiant in the early nineties. D-BSP captures part of the proximity structure of the computing platform , modeling it by suitable decompositions into clusters,… (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)