One Class Clustering Tree for Implementing Many to Many Data Linkage

One-to-many and many-to-many data linkage are important in data mining. In earlier works data linkage is performed among entities of the same type. To link between matching entities of different types in larger datasets a new one-to-many and many to many data linkage method with MapReduce is proposed that links between entities of same and different natures. The proposed method is based on a one-class clustering tree (OCCT) that characterizes the entities that should be linked together. With development of the information technology, the scale of data is increasing quickly. The massive data poses a great challenge for data processing and classification. In order to classify the data, there were several algorithm proposed to efficiently cluster the data. This project deals with scalable random forest algorithm for classifying the advertisement benchmark datasets.