• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Data Mining: Data cube computation and data generalization
 

Data Mining: Data cube computation and data generalization

on

  • 10,164 views

Data Mining: Data cube computation and data generalization

Data Mining: Data cube computation and data generalization

Statistics

Views

Total Views
10,164
Views on SlideShare
10,130
Embed Views
34

Actions

Likes
4
Downloads
0
Comments
1

3 Embeds 34

http://dataminingtools.net 19
http://www.dataminingtools.net 14
http://webcache.googleusercontent.com 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel

11 of 1 previous next

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • Desh insan slide download krne ki permission ku nai di... chu********* sala
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Data Mining: Data cube computation and data generalization Data Mining: Data cube computation and data generalization Presentation Transcript

    • Data Cube Computation and Data Generalization
    • What is Data generalization?
      Data generalization is a process that abstracts a large set of task-relevant data in a database from a relatively low conceptual level to higher conceptual levels.
    • What are efficient methods for Data Cube Computation?
      Different Data cube materialization include 
      Full Cube
      Iceberg Cube
      Closed Cube
      Shell Cube
    • General Strategies for Cube Computation
      1: Sorting, hashing, and grouping.2: Simultaneous aggregation and caching intermediate results.3: Aggregation from the smallest child, when there exist multiple child cuboids.4: The Apriori pruning method can be explored to compute iceberg cubes efficiently
    • What is Apriori Property?
      The Apriori property, in the context of data cubes, states as follows: If a given cell does not satisfy minimum support, then no descendant (i.e., more specialized or detailed version) of the cell will satisfy minimum support either. This property can be used to substantially reduce the computation of iceberg cubes.
    • The Full Cube
      The Multi way Array Aggregation (or simply Multi Way) method computes a full data cube by using a multidimensional array as its basic data structure
      Partition the array into chunks
      Compute aggregates by visiting (i.e., accessing the values at) cube cells
    • BUC: Computing Iceberg Cubes from the Apex Cuboid’s Downward
      BUC stands for “Bottom-Up Construction" , BUC is an algorithm for the computation of sparse and iceberg cubes. Unlike Multi Way, BUC constructs the cube from the apex cuboids' toward the base cuboids'. This allows BUC to share data partitioning costs. This order of processing also allows BUC to prune during construction, using the Apriori property. (for algorithm refer wiki)
    • Development of Data Cube and OLAP Technology
      Discovery-Driven Exploration of Data Cubes Tools need to be developed to assist users in intelligently exploring the huge aggregated space of a data cube. Discovery-driven exploration is such a cube exploration approach.
      Complex Aggregation at Multiple Granularity: Multi feature Cubes Data cubes facilitate the answering of data mining queries as they allow the computation of aggregate data at multiple levels of granularity
    • Constrained Gradient Analysis in Data Cubes
      Constrained multidimensional gradient analysis reduces the search space and derives interesting results. It incorporates the following types of constraints:
      Significance constraint
      Probe constraint
      Gradient constraint
    • Alternative Method for Data Generalization
      Attribute-Oriented Induction for Data CharacterizationThe attribute-oriented induction approach is basically a query-oriented, generalization-based, on-line data analysis technique The general idea of attribute-oriented induction is to first collect the task-relevant data using a database query and then perform generalization based on the examination of the number of distinct values of each attribute in the relevant set of data
    • Cont..
      Attribute generalization is based on the following rule: If there is a large set of distinct values for an attribute in the initial working relation, and there exists a set of generalization operators on the attribute, then a generalization operator should be selected and applied to the attribute.
    • Different ways to control a generalization process
      The control of how high an attribute should be generalized is typically quite subjective. The control of this process is called attribute generalization control.
      Attribute generalization threshold control
      Generalized relation threshold control
    • Mining Classes
      Data collection
      Dimension relevance analysis
      Synchronous generalization
      Presentation of the derived comparison
    • 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