Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results

Fuzzy Interpolation Systems and Applications

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications

Application of expert systems in project management decision aiding

The feasibility of developing an expert systems-based project management decision aid to enhance the performance of NASA project managers was assessed. The research effort included extensive literature reviews in the areas of project management, project management decision aiding, expert systems technology, and human-computer interface engineering. Literature reviews were augmented by focused interviews with NASA managers. Time estimation for project scheduling was identified as the target activity for decision augmentation, and a design was developed for an Integrated NASA System for Intelligent Time Estimation (INSITE). The proposed INSITE design was judged feasible with a low level of risk. A partial proof-of-concept experiment was performed and was successful. Specific conclusions drawn from the research and analyses are included. The INSITE concept is potentially applicable in any management sphere, commercial or government, where time estimation is required for project scheduling. As project scheduling is a nearly universal management activity, the range of possibilities is considerable. The INSITE concept also holds potential for enhancing other management tasks, especially in areas such as cost estimation, where estimation-by-analogy is already a proven method

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Outliers in rules - the comparision of LOF, COF and KMEANS algorithms

bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently

Outliers in Covid 19 data based on Rule representation - the analysis of LOF algorithm

Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021; 08-10.092021, SzczecinThe article concerns the detection of outliers in rule-based knowledge bases containing data on Covid 19 cases. The authors move from the automatic generation of a rule-based knowledge base from source data by clustering rules in the knowledge base to optimize inference processes and to detecting unusual rules allowing for the optimal structure of rule groups. The paper presents a two-phase procedure, wherein in the first phase, we look for the optimal structure of rule clusters when there are outlier rules in the knowledge base. In the second phase, we detect outliers in the rules using the LOF (Local Outlier Factor) algorithm. Then we eliminate the unusual rules from the database and check whether the selected cluster quality measures are responded positively to the elimination of outliers, which would indicate that the rules were rightly considered outliers. The performed experiments confirmed the effectiveness of the LOF algorithm and selected cluster quality measures in the context of detecting atypical rules. The detection of such rules can support knowledge engineers or domain experts in knowledge mining to improve the completeness of the knowledge base, which is usually the basis of the decision support system

An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi-Sugeno-Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: 1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base, and 2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases