Efficient chain structure for high-utility sequential pattern mining

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates

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

Literature Review on Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

This paper presenting a survey on finding itemsets with high utility. For finding itemsets there are many algorithms but those algorithms having a problem of producing a large number of candidate itemsets for high utility itemsets which reduces mining performance in terms of execution. Here we mainly focus on two algorithms utility pattern growth (UP-Growth) and UP-Growth+. Those algorithms are used for mining high utility itemsets, where effective methods are used for pruning candidate itemsets. Mining high utility itemsets Keep in a special data structure called UP-Tree. This, compact tree structure, UP-Tree, is used for make possible the mining performance and avoid scanning original database repeatedly. In this for generation of candidate itemsets only two scans of database. Another proposed algorithms UP Growth+ reduces the number of candidates effectively. It also has better performance than other algorithms in terms of runtime, especially when databases contain huge amount of long transactions. Utility-based data mining is a new research area which is interested in all types of utility factors in data mining processes. In which utility factors are targeted at integrate utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utility based descriptive data mining. Utility based data mining is used for finding itemsets that contribute most to the total utility in that database