کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
396511 670362 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Rough set approach for clustering categorical data using information-theoretic dependency measure
ترجمه فارسی عنوان
رویکرد خشن برای خوشه بندی داده های قطعی با استفاده از معیار وابستگی نظری اطلاعاتی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

A variety of clustering algorithms exists to group objects having similar characteristics. But the implementations of many of those algorithms are challenging in the process of dealing with categorical data. While some of the algorithms cannot handle categorical data, others are unable to handle uncertainty within categorical data in nature. This is prerequisite for clustering categorical data which also deal with uncertainty. An algorithm, termed minimum-minimum roughness (MMR) was proposed, which uses the rough set theory in order to deal with the above problems in clustering categorical data. Later many algorithms has developed to improve the handling of hybrid data. This research proposes information-theoretic dependency roughness (ITDR), another technique for categorical data clustering taking into account information-theoretic attributes dependencies degree of categorical-valued information systems. In addition, it is second to none of all its predecessors; MMR, MMeR, SDR and standard-deviation of standard-deviation roughness (SSDR). Experimental results on two benchmark UCI datasets show that ITDR technique is better with the baseline categorical data clustering technique with respect to computational complexity and the purity of clusters.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Systems - Volume 48, March 2015, Pages 289–295
نویسندگان
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