Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
ACM Computing Surveys (CSUR)
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Parallel Clustering Algorithm for Large Spatial Databases
Data Mining and Knowledge Discovery
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Parallel Clustering Algorithms for Image Processing on Multi-core CPUs
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 03
Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Spectral Clustering with Random Projection and Sampling
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Parallel K-Means Clustering Based on MapReduce
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Fast large-scale spectral clustering by sequential shrinkage optimization
ECIR'07 Proceedings of the 29th European conference on IR research
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globe'11 Proceedings of the 4th international conference on Data management in grid and peer-to-peer systems
Analytics over large-scale multidimensional data: the big data revolution!
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Power iteration clustering (PIC) is a newly developed clustering algorithm. It performs clustering by embedding data points in a low-dimensional subspace derived from the similarity matrix. Compared to traditional clustering algorithms, PIC is simple, fast and relatively scalable. However, it requires the data and its associated similarity matrix fit into memory, which makes the algorithm infeasible for big data applications. This paper attempts to expand PIC's data scalability by implementing a parallel power iteration clustering (p-PIC). While this paper focuses on exploring different parallelization strategies and implementation details for minimizing computation and communication costs, we have also paid great attention to ensuring the algorithm works well on low-end commodity computers (COTS-based clusters and general purpose servers found at most commercial cloud providers). The experimental results demonstrate that the proposed p-PIC algorithm is highly scalable to both data and compute resources.