Generating Summaries for Frequent Univariate Uncertain Pattern

Abstract

In big data related research and applications, discovery of useful knowledge from big data is an important topic. While most studies concerning this topic focus on developing methods for retrieving knowledge, presentation of the discovered knowledge remains a critical issue. A good method of presentation allows ordinary people to quickly understand and utilize the discovered knowledge. In the literature, univariate uncertain data is one kind of big data, and frequent patterns retrieved from univariate uncertain data, i.e., frequent univariate uncertain patterns, represent useful knowledge. However, the number of frequent univariate uncertain patterns is often too large to be dealt with by ordinary user. In other words, a good method of presenting the discovered frequent univariate uncertain patterns is required. To this end, we propose a novel way of summarizing frequent univariate uncertain patterns. We use a hierarchical clustering technique to generate a summary of a set of frequent univariate uncertain patterns. Instead of examining a large number of frequent univariate uncertain patterns, a user only needs to check tens, or perhaps hundreds, of representative frequent univariate uncertain patterns. Experimental results show that the summarization quality of our method is better than the summarization quality of maximum frequent univariate uncertain patterns.