Efficient contrast subspace mining method for categorical data - Florence Sia Fui Sze; Rayner Alfred (Association for Computing Machinery, 2023)
Mining contrast subspace has recently received attention to identify contrast subspace where a query object is most likely similar to a target class but least likely similar to other class. It has many important applications in various domain such as healthcare, security, finance, and business. Tree-based contrast subspace mining method (TB-CS Miner) has been introduced that is capable to identify contrast subspace of query object in categorical data set. However, the effectiveness of the method has not been evaluated thoroughly. Bedsides, the efficiency of the method in finding contrast subspace has not yet been examined. Real world data sets more often than not containing large amount of data. It is important to have a method that can identify contrast subspace of query object not only effectively but also efficiently on large data set. This paper uses various classification methods to further evaluate the effectiveness of the TB-CS Miner and assesses the execution speed of the method in mining contrast subspace. TB-CS Miner is experimentally shown to be significantly faster and as effective in identifying contrast subspace of query object on real world categorical data sets.