Discretization and concept hierarchy generation pdf
File Name: discretization and concept hierarchy generation .zip
- mining data streams in dwdm
- Data Discretization and Concept Hierarchy Generation
- Data discretization in data mining
The topic discussed in the attatchments below is of the course computer science and he subject data mining.
Ballou and G. Enhancing data quality in data warehouse environments. Dasu and T.
mining data streams in dwdm
A concept hierarchy for location. Due to space limitations, not all of the hierarchy nodes are shown, indicated by ellipses between nodes. Many concept hierarchies are implicit within the database schema. Concept Hierarchy reduce the data by collecting and replacing low level concepts such as numeric values for the attribute age by higher level concepts such as young, middle-aged, or senior. Concept hierarchy generation for numeric data is as follows: Binning see sections before Histogram analysis see sections before. The research in this dissertation is an important step forward of concept hierarchy con-struction.
Data Discretization and Concept Hierarchy Generation
Introduction: Data discretization techniques can be used to reduce the number of values for a given continuous attribute by dividing the range of the attribute into intervals. Interval labels can then be used to replace actual data values. Replacing numerous values of a continuous attribute by a small number of interval labels thereby reduces and simplifies the original data. This leads to a concise, easy-to-use, knowledge-level representation of mining results. Discretization techniques can be categorized based on how the discretization is performed, such as whether it uses class information or which direction it proceeds i.
Data Discretization techniques can be used to divide the range of continuous attribute into intervals. Numerous continuous attribute values are replaced by small interval labels. This leads to a concise, easy-to-use, knowledge-level representation of mining results. If the process starts by first finding one or a few points called split points or cut points to split the entire attribute range, and then repeats this recursively on the resulting intervals, then it is called top-down discretization or splitting. If the process starts by considering all of the continuous values as potential split-points, removes some by merging neighborhood values to form intervals, then it is called bottom-up discretization or merging.
Data discretization in data mining
Skip to content. All Homes Search Contact. Each of these properties adds a challenge to data stream mining.
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If data is not put into context, it doesn't do anything to a human or computer. A binary digit, or bit, is the smallest unit of data in computing. How to Organize Computer Documents.
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