HDD: a hypercube division-based algorithm for discretisation

Discretisation, as one of the basic data preparation techniques, has played an important role in data mining. This article introduces a new hypercube division-based (HDD) algorithm for supervised discretisation. The algorithm considers the distribution of both class and continuous attributes and the...

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Bibliographic Details
Published in:International journal of systems science 2011-04, Vol.42 (4), p.557-566
Main Authors: Yang, Ping, Li, Ji-Sheng, Huang, Yong-Xuan
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
class-attribute interdependence maximisation
Classification
Computer science
control theory
systems
Correlation
Data mining
Data processing. List processing. Character string processing
discretisation
Exact sciences and technology
hypercube division
Hypercubes
Mathematical analysis
Memory organisation. Data processing
Software
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Summary:Discretisation, as one of the basic data preparation techniques, has played an important role in data mining. This article introduces a new hypercube division-based (HDD) algorithm for supervised discretisation. The algorithm considers the distribution of both class and continuous attributes and the underlying correlation structure in the data set. It tries to find a minimal set of cut points, which divides the continuous attribute space into a finite number of hypercubes, and the objects within each hypercube belong to the same decision class. Finally, tests are performed on seven mix-mode data sets, and the C5.0 algorithm is used to generate classification rules from the discretised data. Compared with the other three well-known discretisation algorithms, the HDD algorithm can generate a better discretisation scheme, which improves the accuracy of classification and reduces the number of classification rules.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207720903572455