A new feature selection scheme using data distribution factor for transactional data

A new efficient unsupervised feature selection method is proposed to handle transactional data. The proposed feature selection method introduces a new Data Distribution Factor (DDF) to select appropriate clusters. This method combines the compactness and separation together with a newly introduced concept of singleton item. This new feature selection method is computationally inexpensive and is able to deliver very promising results. Four datasets from UCI machine learning repository are used in this studied. The obtained results show that the proposed method is very efficient and able to deliver very reliable results.