Clustering is an unsupervised learning technique used to organize unlabeled data into groups of similar objects. It involves finding a structure in data by grouping objects with similar characteristics together into clusters while keeping objects in different clusters dissimilar. Clustering can simplify data, detect patterns, help construct data concepts, and be useful for an unsupervised learning process. It is applied in areas like data mining, information retrieval, text mining, web analysis, marketing, and medical diagnosis to organize large datasets and discover hidden patterns.

1. Introduction to
Clustering
Technologies
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2. Definition
• Clustering can be considered the most important unsupervised
learning technique; so, as every other problem of this kind, it
deals with finding a structure in a collection of unlabeled data.
• Clustering is “the process of organizing objects into groups
whose members are similar in some way”.
• A cluster is therefore a collection of objects which are
“similar” between them and are “dissimilar” to the objects
belonging to other clusters.

3. Why clustering?
A few good reasons ...
• Simplifications
• Pattern detection
• Useful in data concept construction
• Unsupervised learning process

4. Where to use clustering?
• Data mining
• Information retrieval
• Text mining
• Web analysis
• Marketing
• Medical diagnostic