A majority rules approach to data mining

Knowledge discovery in databases (KDD) offers a methodology for developing tools to extract meaningful knowledge from large volumes of data. We propose a generalized KDD model for supervised training. A main step in this process, data mining, involves the creation of a classification structure that is representative of the concept classes identified in the data set. Data mining incorporates learning which may be supervised or unsupervised and often uses statistical as well as heuristic (machine learning) techniques. Previous research has shown that different supervised models perform better under certain conditions. We tested the extent of overlap of instance classifications between five supervised models in two real world domains. Experimental results showed that in one domain all five models classified 75.8% of the instances identically, correct or incorrect. In the second domain, the corresponding figure was 63.3%. The amount of agreement between models can be used to help determine the nature of the domain and the applicability of a supervised learning approach. We extend the above experimental result and propose a multi model majority rules (MR) data mining technique to learn about the nature of a given domain. We conclude with directions for future work.