Luminita Dumitriu

Learn More
Using concept lattices as a theoretical background for finding association rules [11] has led to designing algorithms like Charm [10], Close [7] or Closet [8]. While they are considered as extremely appropriate when finding concepts for association rules, due to the smaller amount of results, they do not cover a certain area of significant results, namely(More)
In this article we present the Structure of Management Information from SNMP, for all three versions of SNMP, as well as the main differences between them. In the first part, two version of SNMP are presented: version 1, version 2; and in the last part the third version, that uses a security model for information protection, is presented.
INTRODUCTION Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold.(More)
We are presenting an artificial intelligence application designed to perform a reliable recognition of the geographical origin of horticultural products. The system allows more detailed traceability investigations than those required by the present general and specific standards imposed by the European Community legislation. The classification is performed(More)
In this article we are presenting a signal selective amplification method that was used for improving the automatic detection of amphetamines based on their infrared absorptions recorded in the narrow spectral window in which the QCL source of a new portable GC-IRAS spectrometer is emitting (1550-1330 cm<sup>-1</sup>). The expert system developed for class(More)
The greater than ever amount of data used in predictive data mining calls for new and flexible approaches, based on soft computing methods, with the purpose of recognizing the valuable attributes in datasets. The selection of relevant features is an important part of the preprocessing step in pattern recognition, statistics, knowledge discovery and data(More)