Mining Frequent Itemsets from Uncertain Databases using probabilistic support
Radhika Ramesh Naik · 2013
Mining of frequent itemsets is one of the popular knowledge discovery and data mining tasks. The frequent itemset mining algorithms find itemsets from traditional transaction databases, in which the content of each transaction i.e. items is definitely known and precise. There are many real-life applications like location-based services, sensor monitoring systems in which the content of transactions is uncertain. This initiates the requirement of uncertain data mining. The frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard certain transaction databases. The consideration of existential uncertainty of itemsets, indicating the probability that an itemset occurs in a transaction, makes the traditional techniques inapplicable. Hence the mining methods like the Apriori and the tree based mining needs to be modified for handling the uncertain data. The uncertain data has attribute as well as tuple uncertainty. This paper introduces the techniques for mining frequent itemsets from uncertain databases that makes use of the probabilistic support concept which considers the aspects of uncertain data completely.