Requirements and challenges Scalability Clustering all the data instead of only on samples Ability to deal with different types of attributes Numerical, binary, categorical, ordinal, linked, and mixture of the lese Constraint-based clustering User may give inputs on constraints Use domain knowledge to determine input parameters Interpretability and usability Others Discovery of clusters with arbitrary shape Ability to deal with noisy data Incremental clustering and insensitivity to input order High dimensionalitRequirements and Challenges ◼ Scalability ◼ Clustering all the data instead of only on samples ◼ Ability to deal with different types of attributes ◼ Numerical, binary, categorical, ordinal, linked, and mixture of these ◼ Constraint-based clustering ◼ User may give inputs on constraints ◼ Use domain knowledge to determine input parameters ◼ Interpretability and usability ◼ Others ◼ Discovery of clusters with arbitrary shape ◼ Ability to deal with noisy data ◼ Incremental clustering and insensitivity to input order ◼ High dimensionality 9