کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382153 660739 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Clustering using PK-D: A connectivity and density dissimilarity
ترجمه فارسی عنوان
خوشه بندی با استفاده از PK-D: اتصال و تراکم عدم تشابه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• New dissimilarity joining connectivity and density information.
• Clustering using low vector space representation based on the new dissimilarity.
• Interesting clustering application using gene expression and image data.
• Improved clustering quality of simple algorithms like k-means.

We present a new dissimilarity, which combines connectivity and density information. Usually, connectivity and density are conceived as mutually exclusive concepts; however, we discuss a novel procedure to merge both information sources. Once we have calculated the new dissimilarity, we apply MDS in order to find a low dimensional vector space representation. The new data representation can be used for clustering and data visualization, which is not pursued in this paper. Instead we use clustering to estimate the gain from our approach consisting of dissimilarity + MDS. Hence, we analyze the partitions’ quality obtained by clustering high dimensional data with various well known clustering algorithms based on density, connectivity and message passing, as well as simple algorithms like k-means and Hierarchical Clustering (HC). The quality gap between the partitions found by k-means and HC alone compared to k-means and HC using our new low dimensional vector space representation is remarkable. Moreover, our tests using high dimensional gene expression and image data confirm these results and show a steady performance, which surpasses spectral clustering and other algorithms relevant to our work.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 51, 1 June 2016, Pages 151–160
نویسندگان
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