Data Mining: Data cube computation and data generalization


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Data Mining: Data cube computation and data generalization

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Data Mining: Data cube computation and data generalization

  1. 1. Data Cube Computation and Data Generalization<br />
  2. 2. What is Data generalization?<br />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.<br />
  3. 3. What are efficient methods for Data Cube Computation?<br />Different Data cube materialization include <br />Full Cube<br />Iceberg Cube<br />Closed Cube<br />Shell Cube<br />
  4. 4. General Strategies for Cube Computation<br /> 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<br />
  5. 5. What is Apriori Property?<br />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.<br />
  6. 6. The Full Cube<br /> 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<br />Partition the array into chunks<br />Compute aggregates by visiting (i.e., accessing the values at) cube cells<br />
  7. 7. BUC: Computing Iceberg Cubes from the Apex Cuboid’s Downward<br />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)<br />
  8. 8. Development of Data Cube and OLAP Technology<br />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.<br />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<br />
  9. 9. Constrained Gradient Analysis in Data Cubes<br />Constrained multidimensional gradient analysis reduces the search space and derives interesting results. It incorporates the following types of constraints:<br />Significance constraint<br />Probe constraint<br />Gradient constraint<br />
  10. 10. Alternative Method for Data Generalization<br />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<br />
  11. 11. Cont..<br />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.<br />
  12. 12. Different ways to control a generalization process<br /> The control of how high an attribute should be generalized is typically quite subjective. The control of this process is called attribute generalization control.<br />Attribute generalization threshold control<br />Generalized relation threshold control<br />
  13. 13. Mining Classes<br />Data collection<br />Dimension relevance analysis<br />Synchronous generalization<br />Presentation of the derived comparison<br />
  14. 14. Visit more self help tutorials<br />Pick a tutorial of your choice and browse through it at your own pace.<br />The tutorials section is free, self-guiding and will not involve any additional support.<br />Visit us at<br />