Index-Based Solutions for Efficient Density Peak Clustering
Index-Based Solutions for Efficient Density Peak Clustering
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基于索引的高效密度峰聚类解决方案
DOI：
10.1109/tkde.2020.3004221
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发表时间：
2020-02
期刊：
影响因子：
--
通讯作者：
Jiajie Xu
中科院分区：
文献类型：
--
作者：
Zafaryab Rasool;Rui Zhou;Lu Chen;Chengfei Liu;Jiajie Xu
Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter <inline-formula><tex-math notation="LaTeX">$d_c$</tex-math><alternatives><mml:math><mml:msub><mml:mi>d</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="rasool-ieq1-3004221.gif"/></alternatives></inline-formula>, which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index.
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DOI：
10.1109/bigdata.2018.8621953
发表时间：
2018-10
期刊：
2018 IEEE International Conference on Big Data (Big Data)
影响因子：
--
作者：
Fotis Savva;C. Anagnostopoulos;P. Triantafillou
通讯作者：
Fotis Savva;C. Anagnostopoulos;P. Triantafillou
DOI：
10.1145/170088.170403
发表时间：
1993-12
期刊：
--
影响因子：
--
作者：
I. Kamel;C. Faloutsos
通讯作者：
I. Kamel;C. Faloutsos
DOI：
--
发表时间：
2014-06
期刊：
--
影响因子：
--
作者：
J. Leskovec;A. Krevl
通讯作者：
J. Leskovec;A. Krevl
影响因子：
5.3
作者：
Anagnostopoulos, Christos;Savva, Fotis;Triantafillou, Peter
通讯作者：
Triantafillou, Peter
流形数据集基于局部核共享邻居的改进密度峰聚类
影响因子：
3.9
作者：
Cheng Dongdong;Huang Jinlong;Zhang Sulan;Liu Huijun
通讯作者：
Liu Huijun