Comparative Study of different Lazy Learning Associative Classification Methods

Abstract

Abstract Lazy Learning Associative Classification (LLAC) is a promising approach in the field of data mining. It is one of the associative classification methods in which it delays the processing of training datasets until it receives the test instance for the class prediction. Lazy learning associative classification can be constructed in two phases. Subset generation is the first phase and the subset evaluation is the second phase. In the past decades, many lazy learning associative classification methods have been proposed. These algorithms utilize several different methods for subset generation and subset evaluation. This paper focuses on comparative study of different lazy learning associative classification methods.