Data mining is the procedure of extorting patterns from data. At present, it is broadly used in various fields like profiling practices, such as marketing, observation, fraud detection and scientific discovery, bioinformatics research. In this survey, mainly give attention to the classification and clustering of data mining approaches. Data mining includes clustering with difficulties of very large datasets with several classes of different types. This inflicts individual computational need on significant clustering algorithms. Another thing is Classification which is a data mining related to machine learning approach used to identify group membership for data samples. The classification approaches like decision tree induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm bolsos de imitacion de lujo and fuzzy logic techniques are used widely in many areas. The aim of this survey is to give a wide-ranging evaluation of different classification and clustering techniques in data mining. This investigation evidently analysis the clustering and classification in the review and finally concludes which is clustering and classification is better for various fields.