کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
390818 661305 2009 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets
چکیده انگلیسی

Recently many researchers exert their effort on clustering as a primary data mining method for knowledge discovery, but only few of them have focused on uneven dataset. In the last research, we proposed an efficient hierarchical algorithm based on fuzzy graph connectedness—FHC—to discover clusters with arbitrary shapes. In this paper, we present a novel clustering algorithm for uneven dataset—PFHC—which is an extended version based on FHC. In PFHC, dataset is divided into several local spaces firstly according to the data density of distribution, where the data density in any local space is nearly uniform. In order to achieve the goal, local ɛ and λ are used in each local domain to acquire local clustering result by FHC. Then boundary between local areas needs being taken into consideration for combination. Finally local clusters need to be merged to obtain global clusters. As an extension of FHC, PFHC can deal with uneven datasets more effectively and efficiently, and generate better quality clusters than other methods as experiment shows. Furthermore, PFHC is found to be able to process incremental data as well in this work.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 160, Issue 13, 1 July 2009, Pages 1886-1901