دانلود مقاله ISI انگلیسی شماره 79201
ترجمه فارسی عنوان مقاله

طراحی الگوریتم های خوشه بندی مبتنی بر تراکم محاسباتی کارآمد

عنوان انگلیسی
Design of computationally efficient density-based clustering algorithms
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79201 2015 16 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Data & Knowledge Engineering, Volume 95, January 2015, Pages 23–38

ترجمه کلمات کلیدی
خوشه بندی، طبقه بندی، و ارتباط قوانین؛ الگوریتم ها و روش استخراج - DBC DBSCAN سریع؛ مجموعه داده عمل فیزیکی؛ فهرست لرزه ژاپن
کلمات کلیدی انگلیسی
Clustering, classification, and association rules; Mining methods and algorithms; DBSCAN; Fast DBC; Physical action datasets; Seismic catalog of Japan
پیش نمایش مقاله
پیش نمایش مقاله  طراحی الگوریتم های خوشه بندی مبتنی بر تراکم محاسباتی کارآمد

چکیده انگلیسی

The basic DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm uses minimum number of input parameters, very effective to cluster large spatial databases but involves more computational complexity. The present paper proposes a new strategy to reduce the computational complexity associated with the DBSCAN by efficiently implementing new merging criteria at the initial stage of evolution of clusters. Further new density based clustering (DBC) algorithms are proposed considering correlation coefficient as similarity measure. These algorithms though computationally not efficient, found to be effective when there is high similarity between patterns of dataset. The computations associated with DBC based on correlation algorithms are reduced with new cluster merging criteria. Test on several synthetic and real datasets demonstrates that these computationally efficient algorithms are comparable in accuracy to the traditional one. An interesting application of the proposed algorithm has been demonstrated to identify the regional hazard regions present in the seismic catalog of Japan.