Data mining presentation
- 2. Daffodil International University Submitted To: MD. Firoz Hasan Lecturer Daffodil International University Submitted By: Nirnay Mukharjee – 143-15-4421
- 3. • Introduction • Clustering • Why Clustering? • Several working definitions of clustering • Methods of clustering • Applications of clustering
- 4. • Defined as extracting the information from the huge set of data. • Extracting set of patterns from the data set.
- 6. • High dimensionality • Ability to deal with noisy data • Interpretability • Scalability • Ability to deal with different kind of attributes • Discovery of clusters with attribute shape
- 7. • Well separated cluster definition • Centre based cluster definition • Similarity based cluster definition • Density based cluster definition
- 8. • Partitioning Method • Hierarchical Method • Density-based Method • Grid-Based Method • Model-Based Method • Constraint-based Method
- 9. • Suppose we are given a database of n objects, the partitioning method construct partition of data. – Each group contain at least one object. – Each object must belong to exactly one group. • For a given number of partitions, the partitioning method will create an initial partitioning. • Then it uses the iterative relocation technique to improve the partitioning by moving objects from one group to other.
- 10. • This method is based on the notion of density. The basic idea is to continue growing the given cluster as long as the density in the neighbourhood exceeds some threshold i.e. for each data point within a given cluster, the radius of a given cluster has to contain at least a minimum number of points.
- 11. • In this the objects together from a grid. The object space is quantized into finite number of cells that form a grid structure. • AdvantageThe major advantage of this method is fast processing time. • It is dependent only on the number of cells in each dimension in the quantized space.
- 12. • Pattern Recognition • Spatial Data Analysis: • Image Processing • Economic Science (especially market research) • Crime analysis • Bio informatics • Medical Imaging • Robotics • Climatology