高维稀疏数据的结构化倒排文件k均值聚类
Structured Inverted-File k-Means Clustering for High-Dimensional Sparse Data
摘要：
针对大规模高维稀疏数据集，提出了一种结构友好的k均值聚类算法SIVF。算法在时间上的效率通常由诸如相似性计算之类的高成本操作的数量来衡量。然而，在实践中，它在很大程度上取决于该算法如何适应的计算机系统的架构，它是executedon.We建议SIVF采用不变的基于质心对的过滤器（ICP），以减少数据对象和质心的所有集群之间的相似性计算的数量。为了最大限度地提高ICP性能，SIVF利用一个质心集的一个倒置的文件，结构，以减少管道的危险。我们证明在我们的实验中，真正的大规模文档数据集，SIVF操作速度更高，内存消耗比现有的算法。我们的性能分析表明，SIVF实现了更高的速度，通过抑制性能下降的因素，而不是更少的相似性计算的高速缓存未命中和分支误预测的数量。
英文摘要：
This paper presents an architecture-friendly k-means clustering algorithm called SIVF for a large-scale and high-dimensional sparse data set. Algorithm efficiency on time is often measured by the number of costly operations such as similarity calculations. In practice, however, it depends greatly on how the algorithm adapts to an architecture of the computer system which it is executed on. Our proposed SIVF employs invariant centroid-pair based filter (ICP) to decrease the number of similarity calculations between a data object and centroids of all the clusters. To maximize the ICP performance, SIVF exploits for a centroid set an inverted-file that is structured so as to reduce pipeline hazards. We demonstrate in our experiments on real large-scale document data sets that SIVF operates at higher speed and with lower memory consumption than existing algorithms. Our performance analysis reveals that SIVF achieves the higher speed by suppressing performance degradation factors of the number of cache misses and branch mispredictions rather than less similarity calculations.
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