Clustering is a method of grouping objects in such a way that objects with similar features come together, and objects with dissimilar features go apart. It is a common technique for statistical data analysis for Machine Learning and Data Mining. Exploratory data analysis and generalization is also an area that uses clustering.
Clustering belongs to unsupervised data mining. It is not a single specific algorithm, but it is a general method to solve a task. It is not an automatic task, but it is an iterative process of discovery. Therefore, it is necessary to modify data processing and parameter modeling until the result achieves the desired properties. K-means clustering and Hierarchical clustering are two common clustering algorithms in data mining.
Classification is a categorization process that uses a training set of data to recognize, differentiate and understand objects. Classification is a supervised learning technique where a training set and correctly defined observations are available.
The algorithm that implements classification is the classifier whereas the observations are the instances. K-Nearest Neighbor algorithm and decision tree algorithms are the most famous classification algorithms in data mining.
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