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Data Mining: Classification and analysis
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Data Mining: Classification and analysis

Data Mining: Classification and analysis

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    Data Mining: Classification and analysis Data Mining: Classification and analysis Presentation Transcript

    • Data Mining :Classification and Analysis
    • Classification in the process of Data Mining
      It is the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown.
      There are 3 models in which classification can be represented
      IF-THEN rules, A decision tree, Neural network.
    • Classificationof Data Mining Systems
      The kinds of databases mined
      The kinds of knowledge mined
      The kinds of techniques utilized
      The applications adapted
    • What is Cluster Analysis?
      Clustering analyzes data objects without consulting a known class label. Clustering can also facilitate taxonomy formation.
    • What is Outlier Mining ?
      A database may contain data objects that do not comply with the general behavior or model of the data. These data objects are outliers. The analysis of outlier data is referred to as outlier mining.
    • What is Evolution Analysis?
      Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time.
    • What are Data Mining Task Primitives?
      A data mining task can be specified in the form of a data mining query, which is input to the data mining system. A data mining query is defined in terms of data mining task primitives.
    • Integration schemes of Data Base and Data Warehouse systems
      No coupling: No coupling means that a DM system will not utilize any function of a DB or DW system
      Loose coupling: Loose coupling means that a DM system will use some facilities of a DB or DW system, fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results either in a file or in a designated place in a database or data warehouse.
    • Cont..
      Semi tight coupling: Semi tight coupling means that besides linking a DM system to a DB/DW system, efficient implementations of a few essential data mining primitives (identified by the analysis of frequently encountered data mining functions) can be provided in the DB/DW system.
      Tight coupling: Tight coupling means that a DM system is smoothly integrated into the DB/DW system.
    • Some issues encountered in Data Mining
      Mining methodology and user interaction issues
      Mining different kinds of knowledge in databases
      Interactive mining of knowledge at multiple levels of abstraction
      Incorporation of background knowledge
      Data mining query languages and ad hoc data mining
      Presentation and visualization of data mining results
      Handling noisy or incomplete data
      Pattern evaluation—the interestingness problem
    • The performance of data mining system
      Efficiency and scalability of data mining algorithms
      Parallel, distributed, and incremental mining algorithms
      Issues relating to the diversity of database types
      Handling of relational and complex types of data
      Mining information from heterogeneous databases and global information systems
    • Visit more self help tutorials
      Pick a tutorial of your choice and browse through it at your own pace.
      The tutorials section is free, self-guiding and will not involve any additional support.
      Visit us at www.dataminingtools.net