BibTeX:

@article{IJIRSTV1I7041,
     title={A Survey of Classification on Emerging Pattern},
     author={Harsha Parmar},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={127--131},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7041.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Data mining can find the significant chances of frequency between two datasets. It is useful for obtaining knowledge full information from the datasets. It is possible to find significant frequency changes between multiple datasets. This type of knowledge mining is known as Contrast mining. Pattern which is present in one dataset has low frequency and at another dataset it has more frequency then it is known as Emerging Pattern. When pattern is absent in one dataset and present at another dataset then is known as Jumping emerging pattern. Emerging pattern has different types based on significant frequency changes between different datasets. In this paper Generalized EP, CART-based EP, DeEP are mentioned. Classification aims to discover model from training data which is used to predict the class of training instances. Different EP-based classifiers are used to define the class to emerging patterns. Various classification algorithms like border based EP, Constraint base EP, Bayesian Classification by Emerging Pattern are used for classification of training instances.

Classification, Emerging Pattern, Generalized Emerging Pattern, Deep, Bayesian Classification