Finding the Chances and Prediction of Cancer through Apriori Algorithm with Transaction Reduction

Frequent pattern mining is an important task of data mining. It is essential for mining association, relevant and interesting links. In addition, it is widely used in data classification, clustering and other data mining tasks. Many effective, scalable algorithms have been developed in terms of frequent pattern mining. The Apriori algorithm is a classical frequent item sets generation algorithm and a milestone in the development of data mining. In this paper we apply the apriori algorithm with transaction reduction on cancer symptoms. We consider five different types of cancer and according to the classification we generate the candidate sets and minimum support to find the spreading of cancer. By this we can find the symptoms by which the cancer is spreading more and also about the highest spreading cancer type.