Item-centric mining of frequent patterns from big uncertain data

Abstract

High volumes of wide varieties of valuable data of different veracity (e.g., imprecise and uncertain data) can be easily generated or collected at a high velocity for various knowledge-based and intelligent information & engineering systems in many real-life situations. Embedded in these big data is valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. In this article, we present an item-centric algorithm for mining frequent patterns from big uncertain data. Evaluation results show the effectiveness of our algorithm in item-centric mining of frequent patterns from big uncertain data.