Mining High Utility Sequential Patterns from Uncertain Web Access Sequences using the PL-WAP

In general, the web access patterns are retrieved from the web access sequence databases using various sequential pattern algorithms such as GSP, WAP, and PLWAP tree. However, these algorithms do not consider sequential data with quantity (internal utility) (e.g., the amount of the time spent by the user on a web page) and quality (external utility) (e.g., the rating of a web page in a website) information. These algorithms also do not work on uncertain sequential items (e.g., purchased products) having probability (0, 1). Factoring in the utility and uncertainty of each sequence item provides more product information that can be beneficial in mining profitable patterns from company’s websites. For example, a customer can purchase a bottle of ink more frequently than a printer but the purchase of a single printer can yield more profit to the business owner than the purchase of multiple bottles of ink. Most existing traditional uncertain sequential pattern algorithms such as U-Apriori, UF-Growth, and U-PLWAP do not include the utility measures. In U-PLWAP, the web sequences are derived from web log data without including the time spent by the user and the web pages are not associated with any rating. By considering these two utilities, sometimes the items with lower existential probability can be more profitable to the website owner. In utility based traditional algorithms, the only algorithm related to both uncertain and high utility is the PHUI-UP algorithm which considers the probability and utility as different entities and the retrieved patterns are not dependent with both due to two different thresholds, and it does not mine uncertain web access database sequences. This thesis proposes the algorithm HUU-PLWAP miner for mining uncertain sequential patterns with internal and external utility information using PLWAP tree approach that cut down on several database scans of level-wise approaches. HUU-PLWAP uses uncertain internal utility values (derived from sequence uncertainty model) and the constant external utility values (predefined) to retrieve the high utility sequential patterns from uncertain web access sequence databases with the help of U-PLWAP methodology. Experiments show that HUU-PLWAP is at least 95% faster than U-PLWAP, and 75% faster than the PHUI-UP algorithm

Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.acceptedVersio