Fuzzy pattern cluster scheme for breast cancer datasets

Data mining techniques are used for the knowledge discovery process under the large data set environment. Clustering techniques are used to group up the relevant data sets. Hierarchical and partitioned clustering techniques are used for the clustering process. The clustering process is the complex task with high process time. The pattern extraction scheme is applied to find frequent item sets. Association rule mining techniques are applied to carry out the pattern extraction process. The pattern extraction scheme and the clustering scheme are integrated in the simultaneous pattern extraction and clustering scheme. The clustering process is improved with pattern comparison and transaction transfer process. The simultaneous clustering scheme is implemented to analyze the cancer patient diagnosis reports. The cluster accuracy is represented using the fitness values. The system is enhanced with the K-means clustering algorithm. Fuzzy clustering used to optimize the system.