Gaza Strip resulted in humanitarian crisis. The two reasons behind this shortage, as stated by Gaza Electricity Distribution Company (GEDCO) are: the high rate of electricity consumption and the electricity subscribers' low rate of payment. In this paper, data mining methods are applied to seven months of electricity bills data set for Home-Type subscribers. Firstly data preparation and preprocessing is conducted; secondly, different methods of data mining are applied which are: outlier, clustering, association, and classification. The discovered patterns are interpreted to help build an association and classification model to assist overcoming electricity shortage problems. The model will help GEDCO on focusing to increase the number of bills payers and hence increase its the revenue, which will eventually result in increasing the Electricity that company can distribute to subscribers
