An Efficient Algorithm for Computing Entropic Measures of Feature Subsets

International audienceEntropic measures such as conditional entropy or mutual information have been used numerous times in pattern mining, for instance to characterize valuable itemsets or approximate functional dependencies. Strangely enough the fundamental problem of designing efficient algorithms to compute entropy of subsets of features (or mutual information of feature subsets relatively to some target feature) has received little attention compared to the analog problem of computing frequency of itemsets. The present article proposes to fill this gap: it introduces a fast and scalable method that computes entropy and mutual information for a large number of feature subsets by adopting the divide and conquer strategy used by FP-growth-one of the most efficient frequent itemset mining algorithm. In order to illustrate its practical interest, the algorithm is then used to solve the recently introduced problem of mining reliable approximate functional dependencies. It finally provides empirical evidences that in the context of non-redundant pattern extraction, the proposed method outperforms existing algorithms for both speed and scalability

The ATLAS DAQ and Event Filter prototype "-1" project

A project has been approved by the ATLAS Collaboration for the design and implementation of a Data Acquisition and Event Filter prototype, based on the functional architecture described in the ATLAS Technical Proposal. The prototype consists of a full "vertical" slice of the ATLAS Data Acquisition and Event Filter architecture, including all the hardware and software elements of the data flow, its control and monitoring as well as all the elements of a complete on-line system. This paper outlines the project, its goals, structure, schedule and current status and describes details of the system architecture and its components. (C) 1998 Elsevier Science B.V