Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Data Mining


Proposed Parallel Association Rules Algorithm

Emad kadhiem Jabbar; Waheed Abd Al-Kadhiem Salman

Engineering and Technology Journal, 2014, Volume 32, Issue 1, Pages 157-168

Data mining is an advanced technique for extracting knowledge from a large amount of data for classification, prediction, estimation, clustering or association rules or any activities, which need decision. Mining for associations rules between items in large transactional distributed databases is a central problem in the field of knowledge discovery. When distributed databases are merged at single machine to mining knowledge it will require a large capacity of storage, long execution time in addition to transferring a huge volume of data over network might take extremely long time and also require an unbearable financial cost. In this paper an algorithm is presented toward saving communication cost over the network, central storage cost requirements, and accelerating required execution time. In this paper a new algorithm is proposed, called Proposed Parallel Association Rules Algorithm (PPARA) which aims to extract association rules from one record only for each site from distributed association rules in parallel instead of extracting association rules from huge quantity of distributed data at several sites in parallel, and that is through collecting the one record of local association rules from each site and storing it, these Local Association Rules turn in to produce global association rules over distributed systems in parallel.

Classification of Images Using Decision Tree

Emad K. Jabbar; Mayada jabbar kelain

Engineering and Technology Journal, 2013, Volume 31, Issue 6, Pages 728-739

In this paper, the proposed system is based on texture features classification for multi object images by using decision tree (ID3) algorithm. The proposed system uses image segment tile base to reduce the block effect and uses (low low) Wavelet Haar to reduce image size without loss of any important information. The image texture features like (Entropy, Homogeneity, Energy, Inverse Different Moment (IDM), Contrast and Mean) are extracted from image to build database features. All the texture features extracted from the training images are coded into database features code. ID3 algorithm uses database features code for classification of images into different classes. Splitting rules for growing ID3 algorithm are Entropy, Information Gain used to build database rules, which depend on if_then format. The proposed algorithm is experimented on to test image database with 375 images for 5 classes and uses accuracy measure. In the experimental tests 88% of the images are correctly classified and the design of the proposed system in general is enough to allow other classes and extension of the set of classification classes.