Association rules discovery is one of the most important tasks in Knowledge Discovery in Data Bases. Since the initial APRIORI algorithm, many efforts have been done in order to develop efficient algorithms. It is well known that APRIORI-like algorithms within the (unsatisfying) support/confidence framework may produce huge amounts of rules and thus one of the most important steps in associatio...

In this paper we describe the final version of a knowledge discovery system, Telecommunication Network Alarm Sequence Analyzer (TASA), for telecommunication networks alarm data analysis. The system is based on the discovery of recurrent, temporal patterns of alarms in databases; these patterns, episode rules, can be used in the construction of real-time alarm correlation systems. Also associati...

This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student’s usage of the different activities and modules provided by this e-learning system and the final marks obtained in the ...

In this paper, we present a methodology, called Semantic Graph Mining, for computer-aided extraction of actionable rules from consolidated semantic graphs of statements. First, generate semantic annotations of a set of heterogeneous knowledge/information resources in terms of domain ontology. Second, merge a semantic graph by means of semantic integration of the annotated resources. Third, disc...

Automated discovery of rules is, due to its applicability, one of the most fundamental and important method in Knowledge Discovery in Databases(KDD). It has been an active research area in the recent past. This paper presents a classification algorithm based on Evolutionary Approach(EA) that discovers interesting classification rules in the form If P Then D. A flexible encoding scheme, genetic ...

A new multi-objective evolutionary model for subgroup discovery with fuzzy rules is presented in this paper. The method resolves subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the populat...

This paper describes the use of data mining methods in an e-learning system for providing feedback to courseware authors. The discovered information is presented in the form of prediction rules since these are highly comprehensible and they show important relationships among the presented data. The rules will be used to improve courseware, particularly Adaptive Systems for Web-based Education (...

We have developed a rule learning system that employed Grammar Based Genetic Program ming for knowledge discovery from databases A grammar is used as a template for the rule format Grammar Based Genetic Program ming can guide the evolution and the discovery of meaningful rules The technique Token Competition is used to achieve the learning of multiple rules simultaneously We have applied the ap...

With the enactment of the Federal Rules of Civil Procedure (FRCP), the legal discovery process in healthcare is beginning a radical transformation. The rules governing the discovery of electronic information and the legal process will vary by court jurisdiction (federal, state, or local) as well as the nature, size, and type of case under litigation (civil, criminal, or class action). To prepar...

Citation: Bonadonna F and Killian JM (2015) A Knowledge-Based System for Evaluation of CIDP Diagnostic Criteria in a Database with 26,000 Nerve Conduction Studies: Computer-Aided Creation of Heuristic Rules and Discovery of New Diagnostic Criteria. Front. ICT 2:26. doi: 10.3389/fict.2015.00026 A Knowledge-Based System for Evaluation of CIDP Diagnostic Criteria in a Database with 26,000 Nerve Co...