反向文件k-Means聚类：性能分析
Inverted-File k-Means Clustering: Performance Analysis
摘要：
提出了一种适用于大规模稀疏数据集的倒置文件k均值聚类算法（IVF）。给定这样的数据集，IVF以高速和低内存消耗有效地工作，这与标准劳埃德算法保持相同的解决方案。高性能源于两种不同的数据表示。一个是对象和均值特征向量的稀疏表达式。另一种是一组平均特征向量的倒排文件数据结构。为了确认这些表示的效果，我们设计了三个算法，使用不同的数据结构和表达式进行比较。我们的实验表明，IVF实现更好的性能比设计的算法时，他们被应用到大规模的真实文档数据集在现代计算机系统配备了超标量无序处理器和一个深层次的存储系统。我们还介绍了一个简单而实用的每指令时钟周期（CPI）模型的速度性能分析。分析结果表明，IVF抑制了三个性能下降的因素：高速缓存未命中，分支误预测，和完成的指令的数量。
英文摘要：
This paper presents an inverted-file k-means clustering algorithm (IVF) suitable for a large-scale sparse data set with potentially numerous classes. Given such a data set, IVF efficiently works at high-speed and with low memory consumption, which keeps the same solution as a standard Lloyd's algorithm. The high performance arises from two distinct data representations. One is a sparse expression for both the object and mean feature vectors. The other is an inverted-file data structure for a set of the mean feature vectors. To confirm the effect of these representations, we design three algorithms using distinct data structures and expressions for comparison. We experimentally demonstrate that IVF achieves better performance than the designed algorithms when they are applied to large-scale real document data sets in a modern computer system equipped with superscalar out-of-order processors and a deep hierarchical memory system. We also introduce a simple yet practical clock-cycle per instruction (CPI) model for speed-performance analysis. Analytical results reveal that IVF suppresses three performance degradation factors: the numbers of cache misses, branch mispredictions, and the completed instructions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
