数据泛化 data cube computation and data generalization.ppt,Data Mining: Concepts and Techniques Data Mining: Concepts and Techniques — Chapter 4 — Chapter 4: Data Cube Computation and Data Generalization Efficient Computation of Data Cubes Exploration
chapter446datacubecomputationandgeneralization 系统标签: cubecomputationdatageneralizationchaptercubing 2/13/2015DataMining:ConceptsandTechniques1DataMining:ConceptsandTechniques—Chapter4—2/13/2015DataMining:ConceptsandTechniques22/13/2015DataMining:ConceptsandTechniques3Chapter4:DataCubeComputationandDataGeneralizatio...
1 where two tasks are performed in the data preprocessing phase, including data integration and data cleaning. Multidimensional data exploration then follows, incorporating benchmark reports and cube lattice selection with OLAP exploration. After, a pre-mining phase incorporates data transformation and ...
A renderable geometry the may be populated with shapes and data series. The geometry defines a set of dimensions to be applied to the shapes. The geometry further defines and enforc
C. Data Cube Aggregation Data cube aggregation is a data mining technique that involves summarizing and aggregating data along multiple dimensions to create a concise and informative data representation. It is commonly used inonline analytical processing (OLAP)and data warehousing applications to provide...
An overview of data warehouse implementation examines general strategies for efficient data cube computation, OLAP data indexing, and OLAP query processing. Finally, data generalization by attribute-oriented induction is studied. This method uses concept hierarchies to generalize data to multiple levels of...
generalizationandspecializationcanbeperformedonadatacubebyroll-upanddrill-downthisisnotanapproachforconceptdescription,onlyfordatageneralization Limitations:mostcommercialdatacubeimplementationsconfinethetypesofdimensionstosimplenonnumericdataandofmeasurestosimpleaggregatednumericvalues concepthierarchiescanbeautomaticallygenerated...
Wang, “Efficient Computation of Iceberg Cubes with Complex Measures”, SIGMOD01 Fast cubing, space preserving in cube computation Using H-tree for stream cubing Space preserving Intermediate aggregates can be computed incrementally and saved in tree nodes Facilitate computing other cells and multi-...
can the hierarchical approaches. However, due to the high dimensionality of spatial time series, density-based approaches are often not effective. When computing similarity between spatial time series, a filter-and-refine approach (Zhang et al., 2003a) can be used to avoid redundant computation. ...
(chi-square) calculation It shows that like_science_fiction and play_chess are correlated in the group Data Transformation Smoothing: remove noise from data Aggregation: summarization, data cube construction Generalization: concept hierarchy climbing Normalization: scaled to fall within a small, specified...