Abstract
High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds.
Highlights
Knowledge discovery in databases (KDD) [1,2,3,4] aims at finding useful or hidden information in data
To address the challenge of mining high-utility sequential pattern (HUSP) in uncertain data, this paper proposes the task of High Utility-Probability Sequential Pattern Mining (HUPSPM) for mining High Utility-Probability Sequential Patterns (HUPSPs) in uncertain databases
The P-high-utility sequential pattern mining (HUSPM) algorithm adopts pruning strategies 1 to 3 and the projection mechanism to reduce the size of projected databases, which can greatly reduce the time required for processing a dataset
Summary
Knowledge discovery in databases (KDD) [1,2,3,4] aims at finding useful or hidden information in data. Factors such as the profit, weight, and interestingness of patterns are not considered by the pattern discovery process of traditional ARM and FIM To address this limitation, the problem of High-Utility Itemset Mining (HUIM) [5,6,7,8,9] has been introduced. To find patterns having a sequential ordering and reveal relationships between purchased items for customer behavior analysis, Sequential Pattern Mining (SPM) has been proposed It aims at discovering the complete set of frequent sub-sequences that respect a minimum support threshold in a set of sequences of customer transactions. Based on the designed High Sequential-Weighted Utility-Probability Patterns (HSWUPs), a downward-closure property is obtained, and the correctness and completeness of the proposed algorithm for discovering HUPSPs is proven. An experimental study show that this latter algorithm outperforms the baseline algorithm
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