Abstract
Data preprocessing having a pivotal role in data mining ensures reduction in cost by catering inconsistent, incomplete and irrelevant data through data cleansing to assist knowledge workers in making effective decisions through knowledge extraction. Prevalent techniques are not much effective for having more manual effort, increased processing time, less accuracy percentage etc with constrained data volumes. In this research, a comprehensive, semi-automatic preprocessing framework based on hybrid of two machine learning techniques namely Conditional Random Fields (CRF) and Hidden Markov Model (HMM) is devised for data cleansing. Proposed framework is envisaged to be effective and flexible enough to manipulate data set of any size. A bucket of inconsistent dataset (comprising of customer's address directory) of Pakistan Telecommunication Company (PTCL) is used to conduct different experiments for training and validation of proposed approach. Small percentage of semi cleansed data (output of preprocessing) is passed to hybrid of HMM and CRF for learning and rest of the data is used for testing the model. Experiments depict superiority of higher average accuracy of 95.50% for proposed hybrid approach compared to CRF (84.5%) and HMM (88.6%) when applied in separately.