Mining frequent patterns from precise and uncertain data

Abstract

Data mining has gained popularity over the past two decades and has been considered one of the most prominent areas of current database research. Common data mining tasks include finding frequent patterns, clustering and classifying objects, as well as detecting anomalies. To handle these tasks, techniques from different fields--such as database systems, machine learning, statistics, information retrieval, and data visualization--are applied to provide business intelligent (BI) solutions to various real-life problems. In this survey, we focus on the task of frequent pattern mining, which non-trivially extracts implicit, previously unknown and potentially useful information in the form of frequently occurring sets of items. Mined frequent patterns can be considered as building blocks for association rules, which help reveal associative relationships between items or events on the antecedent and the consequent of rules. Here, we describe some classical algorithms, as well as some recent innovative algorithms, for mining precise data (in which users are certain about the presence or absence of data items) and uncertain data (in which users are uncertain about the presence or absence of data items and they only know that data items probably occur).