Data Mining: Classification VS Clustering (cluster analysis)

For someone who is new to Data mining, classification and clustering can seem similar because both data mining algorithms essentially “divide” the datasets into sub-datasets; But there is difference between them and this blog-post, we’ll see exactly that:

CLASSIFICATION CLUSTERING

• We have a Training set containing data that have been previously categorized

• Based on this training set, the algorithms finds the category that the new data points belong to

• We do not know the characteristics of similarity of data in advance

• Using statistical concepts, we split the datasets into sub-datasets such that the Sub-datasets have “Similar” data

Since a Training set exists, we describe this technique as Supervised learning Since Training set is not used, we describe this technique as Unsupervised learning Example:We use training dataset which categorized customers that have churned. Now based on this training set, we can classify whether a customer will churn or not. Example:We use a dataset of customers and split them into sub-datasets of customers with “similar” characteristics. Now this information can be used to market a product to a specific segment of customers that has been identified by clustering algorithm

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