Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, TEXTRISE, th...

The acquisition of syntactic rules is predicated upon the successful discovery of syntactic categories (parts of speech). These do not simply constitute a set, but in fact form a nested hierarchy, which allows rules to apply at different levels of generality. Languages provide a variety of cues to syntactic categorization phonological, semantic and distributional. However, the relative reliabil...

An attribute-oriented rough set method for knowledge discovery in databases is described. The method is based on information generalization, which examines the data at various levels of abstraction, followed by the discovery, analysis and simplification of significant data relationships. First, an attribute-oriented concept tree ascension technique is applied to generalize the information; this...

This paper proposed a robot reinforcement learning method based on learning classifier system. A Learning Classifier System is a accuracy-based machine learning system with gradient descent that combines reinforcement learning and rule discovery system. The genetic algorithm and the covering operator act as innovation discovery components which are responsible for discovering new better reinfor...

Using semantic technologies in grid-systems is considered. We propose the agent-based semantic information service for grid-resource discovery. Ontology is a good approach to resource discovery by providing a formal conceptualization of a grid-system. The approach suggested in the paper allows mapping the elements of ontological languages to the semantic service knowledge base relying on the fi...

In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a memory efficient data structure of a quantitative approach to mine a...

Many applications of knowledge discovery require the knowledge to be consistent with data. Examples include discovering rules for query optimization, database integration, decision support, etc. However, databases usually change over time and make machine-discovered knowledge inconsistent with data. Useful knowledge should be robust against database changes so that it is unlikely to become inco...

A challenge for organizations is to manage electronic information without exposing the organization to sanctions in a later lawsuit. Since organizations do not usually voluntarily disclose internal information, the recent amendments to the Federal Rules of Civil Procedure, applicable to the discovery process, can compel an organization to permit an examination of electronically stored informati...