A Clustering Rule Based Approach for Classification Problems

Abstract

Predictive models, such as rule based classifiers, often have difficulty with incomplete data (e.g., erroneous/missing values). So, this work presents a technique used to reduce the severity of the effects of missing data on the performance of rule base classifiers using divisive data clustering. The Clustering Rule based Approach (CRA) clusters the original training data and builds a separate rule based model on the cluster wise data. The individual models are combined into a larger model and evaluated against test data. The effects of the missing attribute information for ordered and unordered rule sets is evaluated and the collective model (CRA) is experimentally used to show that its performance is less affected than the traditional model when the test data has missing attribute values, thus making it more resilient and robust to missing data.