Impact of Clustering Parameters on the Efficiency of the Knowledge Mining Process in Rule-based Knowledge Bases

Abstract

Abstract. In this work the subject of the application of clustering as a knowledge extraction method from real-world data is discussed. The authors analyze an influence of different clustering parameters on the quality of the created structure of rules clusters and the efficiency of the knowledge mining process for rules / rules clusters. The goal of the experiments was to measure the impact of clustering parameters on the efficiency of the knowledge mining process in rulebased knowledge bases denoted by the size of the created clusters or the size of the representatives. Some parameters guarantee to produce shorter/longer representatives of the created rules clusters as well as smaller/greater clusters sizes.

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

“…The similarity value can be obtained by using one of a various possible similarity measures. The author dealt with the influence of measures of similarity on the clustering quality in [29,30]. In [29], nine various measures were described and analysed: SMC (simple matching coefficient) and its modification wSMC (weighted simple matching coefficient), Gower's measure (widely known in the literature), two measures used for information search in large text files (OF and IOF) and four measures based on the probability of occurrence for a given feature in the description of a rule or a group of rules (Goodall's measures) [27,28].…”

“…In this research, the author uses the same set of similarity measures (in the experimental stage, each of these methods was used). The measures have been widely described by the author in [29,30]; therefore, the issue is not discussed again in this work. For example, the similarity value sim f based on the wSMC equals 1 if rules r i and r j contain the same value for the f attribute.…”