Knowledge Exploration in Medical Rule-Based Knowledge Bases

Nowak-Brzezińska, Agnieszka; Rybotycki, Tomasz; Simiński, Roman; Przybyła–Kasperek, Małgorzata

doi:10.1007/978-3-319-67077-5_15

Cited by 1 publication

(2 citation statements)

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“…There are many possible ways of describing inter-cluster similarity. The most popular among them are known as Single Link (SL), Complete Link (CoL), Average Link (AL) and Centroid Link (CL) [3,4,11].…”

Section: Inter-cluster Similarity Measuresmentioning

confidence: 99%

“…It is first application capable of working on raw KBs as generated from RSES. It was successfully used in previous researches to group and visualize medical knowledge bases generated from artificial data sets available on [15] as well as one generated from real medical data [11]. Moreover, it aggregates functionalities of both clustering and visualization software, making it universal tool for exploring KBs.…”

Section: Cluvismentioning

confidence: 99%

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

Nowak-Brzezińska

Rybotycki

2017

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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.

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Section: Inter-cluster Similarity Measuresmentioning

confidence: 99%

Section: Cluvismentioning

confidence: 99%