## A Data-Clustering Algorithm On Distributed Memory Multiprocessors (2000)

### Cached

### Download Links

- [miles.cnuce.cnr.it]
- [www.cs.utexas.edu]
- [www.cs.utexas.edu]
- [www.cs.utexas.edu]
- DBLP

### Other Repositories/Bibliography

Venue: | In Large-Scale Parallel Data Mining, Lecture Notes in Artificial Intelligence |

Citations: | 95 - 1 self |

### BibTeX

@INPROCEEDINGS{Dhillon00adata-clustering,

author = {Inderjit Dhillon and Dharmendra Modha},

title = {A Data-Clustering Algorithm On Distributed Memory Multiprocessors},

booktitle = {In Large-Scale Parallel Data Mining, Lecture Notes in Artificial Intelligence},

year = {2000},

pages = {245--260}

}

### Years of Citing Articles

### OpenURL

### Abstract

To cluster increasingly massive data sets that are common today in data and text mining, we propose a parallel implementation of the k-means clustering algorithm based on the message passing model. The proposed algorithm exploits the inherent data-parallelism in the k-means algorithm. We analytically show that the speedup and the scaleup of our algorithm approach the optimal as the number of data points increases. We implemented our algorithm on an IBM POWERparallel SP2 with a maximum of 16 nodes. On typical test data sets, we observe nearly linear relative speedups, for example, 15.62 on 16 nodes, and essentially linear scaleup in the size of the data set and in the number of clusters desired. For a 2 gigabyte test data set, our implementation drives the 16 node SP2 at more than 1.8 gigaflops. Keywords: k-means, data mining, massive data sets, message-passing, text mining. 1 Introduction Data sets measuring in gigabytes and even terabytes are now quite common in data and text minin...