S
Shengzhong Feng
Researcher at Chinese Academy of Sciences
Publications - 26
Citations - 1011
Shengzhong Feng is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Cluster analysis & Social network analysis. The author has an hindex of 9, co-authored 26 publications receiving 919 citations.
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Proceedings ArticleDOI
MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce
TL;DR: This paper proposes an efficient parallel density-based clustering algorithm and implements it by a 4-stages MapReduce paradigm and adopts a quick partitioning strategy for large scale non-indexed data.
Journal ArticleDOI
Topic oriented community detection through social objects and link analysis in social networks
Zhongying Zhao,Shengzhong Feng,Qiang Wang,Joshua Zhexue Huang,Graham J. Williams,Jianping Fan +5 more
TL;DR: A topic oriented community detection approach which combines both social objects clustering and link analysis, which can achieve a better performance when the topics are at least as important as the links to the analysis.
Journal ArticleDOI
MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data
TL;DR: MR-DBSCAN is presented, a scalable DBSCAN algorithm using MapReduce that achieves desirable load balancing even in the context of heavily skewed data and proposes a novel data partitioning method based on computation cost estimation.
Journal ArticleDOI
Feature selection via maximizing global information gain for text classification
TL;DR: A novel feature selection metric called global information gain (GIG) which can avoid redundancy naturally is proposed and an efficient algorithm called MGIG performs better than other algorithms (IG, mRMR, JMI, DISR) in most cases.
Proceedings ArticleDOI
Balanced parallel FP-Growth with MapReduce
TL;DR: A balanced parallel FP-Growth algorithm BPFP is proposed, based on the PFP algorithm, which parallelizes FP-growth in the MapReduce approach and outperformed the P FP which uses some simple grouping strategy.