• Examples of Clustering Applications
  • What Is a Good Clustering
  • Requirements for Clustering in Data Mining
    		•

    No Slide Title




    Download 0.5 Mb.
    bet1/7
    Sana26.04.2024
    Hajmi0.5 Mb.
    #208045
      1   2   3   4   5   6   7
    Bog'liq
    xudo xoxlasa tushadi99%, 3-labarotoriya ishi Saralash usul va algoritmlarini tadqiq qilis, cmd buyruqlari, Incremental model nima, 1matematik, word sAM 1 savol, Документ Microsoft Word (4), Ma\'ruzalar (2), ЛАБОРАТОРНАЯ РАБОТА N1, Dasturlash 2, Ariza, Qalandarova Gulshoda, 1648631455, 1650692784, 1651669892 (2)

    Lecture 10 Clustering

    Preview

    • Introduction
    • Partitioning methods
    • Hierarchical methods
    • Model-based methods
    • Density-based methods

    What is Clustering?

    • Cluster: a collection of data objects
      • Similar to one another within the same cluster
      • Dissimilar to the objects in other clusters
    • Cluster analysis
      • Grouping a set of data objects into clusters
    • Clustering is unsupervised classification: no predefined classes
    • Typical applications

    Examples of Clustering Applications

    • Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs
    • Land use: Identification of areas of similar land use in an earth observation database
    • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost
    • Urban planning: Identifying groups of houses according to their house type, value, and geographical location
    • Seismology: Observed earth quake epicenters should be clustered along continent faults

    What Is a Good Clustering?

    • A good clustering method will produce clusters with
      • High intra-class similarity
      • Low inter-class similarity
    • Precise definition of clustering quality is difficult
      • Application-dependent
      • Ultimately subjective

    Requirements for Clustering in Data Mining

    • Scalability
    • Ability to deal with different types of attributes
    • Discovery of clusters with arbitrary shape
    • Minimal domain knowledge required to determine input parameters
    • Ability to deal with noise and outliers
    • Insensitivity to order of input records
    • Robustness wrt high dimensionality
    • Incorporation of user-specified constraints
    • Interpretability and usability

    Download 0.5 Mb.
      1   2   3   4   5   6   7




    Download 0.5 Mb.