Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating "possible worlds" of the uncertain data, on which we subsequently run optimized frequent itemset mining algorithms. As such we gain efficiency at a surprisingly low loss in accuracy. These is confirmed by a statistical and an empirical evaluation on real and synthetic data
Uncertainty in various domains implies the necessity for various data mining techniques and algorith...
Data uncertainty is inherent in many real-world applications such as environmental surveillance and ...
Abstract: Over the past decade, there have been many studies on mining frequent item sets from preci...
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic s...
We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework...
Data uncertainty is inherent in emerging applications such as location-based services, sensor monito...
Abstract. Frequent itemset mining in uncertain transaction databases semantically and computationall...
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic s...
Data uncertainty is inherent in many real-world applications such as environmental surveillance and ...
Data uncertainty is inherent in applications such as sen-sor monitoring systems, location-based serv...
Association rules mining is a common data mining problem that explores the relationships among items...
In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncer...
Researchers have recently defined and presented the theoretical con-cepts and an algorithm necessary...
The data handled in emerging applications like location-based services, sensor monitoring systems, a...
In recent years, a number of emerging applications, such as sensor monitoring systems, RFID networks...
Uncertainty in various domains implies the necessity for various data mining techniques and algorith...
Data uncertainty is inherent in many real-world applications such as environmental surveillance and ...
Abstract: Over the past decade, there have been many studies on mining frequent item sets from preci...
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic s...
We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework...
Data uncertainty is inherent in emerging applications such as location-based services, sensor monito...
Abstract. Frequent itemset mining in uncertain transaction databases semantically and computationall...
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic s...
Data uncertainty is inherent in many real-world applications such as environmental surveillance and ...
Data uncertainty is inherent in applications such as sen-sor monitoring systems, location-based serv...
Association rules mining is a common data mining problem that explores the relationships among items...
In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncer...
Researchers have recently defined and presented the theoretical con-cepts and an algorithm necessary...
The data handled in emerging applications like location-based services, sensor monitoring systems, a...
In recent years, a number of emerging applications, such as sensor monitoring systems, RFID networks...
Uncertainty in various domains implies the necessity for various data mining techniques and algorith...
Data uncertainty is inherent in many real-world applications such as environmental surveillance and ...
Abstract: Over the past decade, there have been many studies on mining frequent item sets from preci...