FuzzyRULES-II: A new approach to fuzzy rule induction from numerical data

© 2016 The authors and IOS Press. All rights reserved. Data mining is a broad area that integrates research efforts from several fields with the aim of processing large volumes of data into knowledge bases for better decision making. Since numerical and nominal data are equally important in practical data mining applications, dealing with different types of data items are among the most important problems in data mining research and development. This paper introduces a new fuzzy rule induction algorithm, able to deal properly with either numerical or nominal attributes, for the creation of classification and predictive models. To better handle numerical data, fuzzy sets are used to represent intervals in the domains of numerical attributes. Experimental results have shown that the proposed algorithm produces robust and general models that can be used for prediction as well as for classification

Automated fuzzy-clustering for Doctus expert system

Our Knowledge-Based Expert System Shell 'Doctus'1 is capable of deduction also called rule-based reasoning and of induction, which is the symbolic version of reasoning by cases2 . If connected to databases or data warehouses the inductive reasoning of Doctus is also used for data mining. To handle numerical domains Doctus uses statistical clustering algorithm. We define the problem in three steps: how to perform a clustering, which is neither rigid nor sensitive to noise, benefiting from the properties of the application domain, reducing the complexity as much as possible, and supplying the decision maker with useful information enabling the possibility of interaction? In this paper we present the conception of Automated FuzzyClustering using triangular and trapezoidal Fuzzy-sets, which provides overlapping Fuzzy-set covering of the domain

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version