Conceptual clustering and concept hierarchies in knowledge discovery
Xiaohua Tony Hu · Summit (Simon Fraser University) · 1992
Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data.Knowledge discovery from a database is a forrn of machine learning where the discovered knowledge is represented in a highlevel language.The growth in the size and number of existing databases far exceeds human abilities to analyse the data, which creates both a need and an opportunity for extracting knowledge from databases.In this thesis, I propose two algorithms for knowledge discovery in database systems.One algorithm finds knowledge rules associated with concepts in the different levels of the conceptual hierarchy; the algorithm is developed based on earlier attribute-oriented conceptual ascension techniques.The other algorithm combines a conceptual clustering technique and machine learning.It can find three kinds of rules, characteristic rules, inheritance rules, and domain knowledge, even in the absence of 2 conceptual hierarchy.Our methods are simple and efficient.