In this work, the suggested system is a Miner of obtain accuracy and comprehensible classified association rules (MOACCR). In this programming system we aim to extraction knowledge base in the form of classified association rules.Our suggested system consists of three process stages, each one processed to handle one of the main challenges in KDD.In first process stage, we build a novel tool called Developed Random Forest Local Least Square(DRFLLS) to estimate the optimal missing values in the databases having missing values.In the second process stage, we proposed adboosting algorithm as we proposed adboosting algorithm as one of the ensemble classification algorithm to find the class for each record.In the third process stage, we try to satisfy the goal of reducing the dimensions. We suggest using the correlation structure among the predicator variables in order to reduce in three main dimensions (features, samples and value of features). We suggest new algorithm called Frequency Pattern-Knowledge Construction (FP-KC).The suggest algorithm (FP-KC) generation collection of classified association rules. The result is a collection of classified association rules.

Samaher Al_Janabi received her BSc, MSc and PhD degrees in Computer Science. She has published well over thirty five scientific papers and authored one book on new trends of KDD and one book on soft computing techniques. She is a one from five women winner the L'Oreal - UNESCO for Women in Science Levant and Egypt Regional Fellowships 2014.