关键词: 大数据集, 数据簸箕, 随机并行序化原理（RPRP）, 聚类簸箕

Abstract:  Very-large-scale data bring about a great challenge for data mining and knowledge discovery. Dustpan, as a familiar tool, can rapidly differentiate objects into some clusters. Based on the work mechanism of a dustpan, this paper presents a novel learning principle: data dustpan, behind which is a random parallel ranking principle (RPRP), which can be used to efficiently rank objects from a large-scale data set. Then, through using the data dustpan, this paper develops a novel clustering method, called clustering dustpan, and its speed is quick. The experimental results show that the clustering dustpan algorithm is very efficient for organizing data. It is worth noting that the data dustpan can be used to efficiently learn a classifier when dealing with a large-scale data set.

Key words: very-large-scale data set, data dustpan, random parallel ranking principle (RPRP), clustering dustpan