کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
379447 | 659302 | 2007 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Investigating diversity of clustering methods: An empirical comparison
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
The paper aims to shed some light on the question why clustering algorithms, despite being quantitative and hence supposedly objective in nature, yield different and varied results. To do that, we took 10 common clustering algorithms and tested them over four known datasets, used in the literature as baselines with agreed upon clusters. One additional method, Binary-Positive, developed by our team, was added to the analysis. The results affirm the unpredictable nature of the clustering process, point to different assumptions taken by different methods. One conclusion of the study is to carefully choose the appropriate clustering method for any given application.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 63, Issue 1, October 2007, Pages 155–166
Journal: Data & Knowledge Engineering - Volume 63, Issue 1, October 2007, Pages 155–166
نویسندگان
Roy Gelbard, Orit Goldman, Israel Spiegler,