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- W2183658013 abstract "Most of the Machine Learning and Data Mining applications can be applicable only on discrete features. However, data in real world are often continuous in nature. Even for algorithms that can directly deal with continuous features, learning is often less efficient and effective. Hence discretization addresses this issue by finding the intervals of numbers which are more concise to represent and specify. Discretization of continuous attributes is one of the important data preprocessing steps of knowledge extraction. An effective discretization method not only can reduce the demand of system memory and improve the efficiency of data mining and machine learning algorithm, but also make the knowledge extracted from the discretized dataset more compact, easy to be understand and used. In this paper, different types of traditional Supervised and Unsupervised discretization techniques along with examples, as well as their advantages and drawbacks have been discussed." @default.
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- W2183658013 date "2011-01-01" @default.
- W2183658013 modified "2023-10-01" @default.
- W2183658013 title "Comparative Analysis of Supervised and Unsupervised Discretization Techniques" @default.
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