Claudio Lucchese

Learn More
This paper presents a new scalable algorithm for discovering closed frequent itemsets, a lossless and condensed representation of all the frequent itemsets that can be mined from a transactional database. Our algorithm exploits a divide-and-conquer approach and a bitwise vertical representation of the database and adopts a particular visit and partitioning(More)
The research challenge addressed in this paper is to devise effective techniques for identifying <i>task-based sessions</i>, i.e. sets of possibly non contiguous queries issued by the user of a Web Search Engine for carrying out a given <i>task</i>. In order to evaluate and compare different approaches, we built, by means of a manual labeling process, a(More)
The scalability, as well as the effectiveness, of the different Content-based Image Retrieval (CBIR) approaches proposed in literature, is today an important research issue. Given the wealth of images on the Web, CBIR systems must in fact leap towards Web-scale datasets. In this paper, we report on our experience in building a test collection of 100 million(More)
iven a collection of objects, the Similarity Self-Join problem requires to discover all those pairs of objects whose similarity is above a user defined threshold. In this paper we focus on document collections, which are characterized by a sparseness that allows effective pruning strategies. Our contribution is a new parallel algorithm within the MapReduce(More)
The constraint-based pattern discovery paradigm was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In this paper we review and extend the state-of-the-art of the constraints that can be pushed in a(More)
The discovery of patterns in binary dataset has many applications, e.g. in electronic commerce, TCP/IP networking, Web usage logging, etc. Still, this is a very challenging task in many respects: overlapping vs. non overlapping patterns, presence of noise, extraction of the most important patterns only. In this paper we formalize the problem of discovering(More)
We present several exact and highly scalable local pattern sampling algorithms. They can be used as an alternative to exhaustive local pattern discovery methods (e.g, frequent set mining or optimistic-estimator-based subgroup discovery) and can substantially improve efficiency as well as controllability of pattern discovery processes. While previous(More)
Constrained frequent patterns and closed frequent patterns are two paradigms aimed at reducing the set of extracted patterns to a smaller, more interesting, subset. Although a lot of work has been done with both these paradigms, there is still confusion around the mining problem obtained by joining closed and constrained frequent patterns in a unique(More)
One of the main problems raising up in the frequent closed itemsetsmining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance(More)
We introduce <i>Dexter</i>, an open source framework for entity linking. The entity linking task aims at identifying all the small text fragments in a document referring to an entity contained in a given knowledge base, e.g., Wikipedia. The annotation is usually organized in three tasks. Given an input document the first task consists in discovering the(More)