کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
379196 | 659273 | 2007 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
MMR: An algorithm for clustering categorical data using Rough Set Theory
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today’s databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min–Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 63, Issue 3, December 2007, Pages 879–893
Journal: Data & Knowledge Engineering - Volume 63, Issue 3, December 2007, Pages 879–893
نویسندگان
Darshit Parmar, Teresa Wu, Jennifer Blackhurst,