Cluster analysis

Data Mining: Classification VS Clustering (cluster analysis)

Standard

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:

CLASSIFICATIONCLUSTERING
  • 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 learningSince 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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