Rough Sets Similarity-Based Learning from Databases

Manydata mining algorithms developed recently are based on inductive learning methods. Very few are based on similarity-based learning. However, similarity-based learning accrues advantages, such as simple representations for concept descriptions, low incremental learning costs, small storage requirements, etc. Wepresent a similarity-based learning method from databases in the context of roughset theory. Unlike the previous similarity-based learning methods, which only consider the syntactic distance between instances and treat all attributes equally important in the similarity measure, our method can analyse the attribute in the databases by using rough set theory and identify the relevant attributes to the task attributes. Wealso eliminate superfluous attributes for the task attribute and assign a weight to the relevant attributes according to their significance to the task attributes. Oursimilarity measure takes into account the semantic information embeddedin the databases.

• Type: Conference
• Year: 1995
• Where: KDD
• Authors: Xiaohua Hu, Nick Cercone
• Keywords: Data Mining, KDD 1995, Learning Methods, Similarity-based Learning, Similarity-based Learning Methods