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

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Data Mining: Data cube computation and data generalization

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  • 11.
  • 12.
  • 13.
  • 14.