Efficient algorithms for mining constrained frequent patterns from uncertain data

Abstract

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject of numerous studies since its introduction. Most of these studies find all the frequent patterns from collection of precise data, in which the items within each datum or transaction are definitely known and precise. However, there are many real-life situations in which the user is interested in only some tiny portions of these frequent patterns. Finding all frequent patterns would then be redundant and waste lots of computation. This calls for constrained mining, which aims to find only those frequent patterns that are interesting to the user. Moreover, there are also many reallife situations in which the data are uncertain. This calls for uncertain data mining. In this paper, we propose an algorithm to efficiently find constrained frequent patterns from collections of uncertain data.