This aim of this project is to apply a series of pattern detection Data Mining algorithms to accurately identify during classroom test exams. To detect if a pattern could be identified on the answer keys between students not attributable to chance alone, multivariate statistics tools were used to determine whether there was any association pattern among the students.  Hierarchical Clustering and Dendrogram Tree were used to identify the grouping affinity behavior related to exam cheating pattern.  Authors also used Heat Map to identify and recognize patterns in exam scores using visual analysis.   The authors also selected the top 20% of questions considered the most difficult ones in order to increase the detection power.  The probability of picking the same wrong answers on the difficult questions are even more unlikely by chance alone as compared to picking the right answers for the easy questions.  It is statistically even more improbable that students would unintentionally select the same wrong answers on difficult questions, and therefore provides very evidence of cheating.  Principle component analysis was also used to identify pairs of students who cheated, with.  The predictive model approach using Data Mining tools was very powerful for analysis of the complex exam cheating patterns.