Abstract
Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.
Highlights
Analyzing symbolic data to discover frequently co-occurring symbols is a problem called Frequent Itemset Mining (FIM) [1], [2], [3], [4], [5] and [7]
Discovering periodic patterns in a customer transaction database is the task of identifying itemsets that periodically appear in a sequence of transactions
By using them together in a novel algorithm called PFPM (Periodic Frequent Pattern Miner), more flexibility is given to users to select patterns meeting specific periodic requirements
Summary
Analyzing symbolic data to discover frequently co-occurring symbols is a problem called Frequent Itemset Mining (FIM) [1], [2], [3], [4], [5] and [7]. Many studies have proposed efficient techniques to enumerate all frequent itemsets from a binary database, and numerous applications of these techniques have been presented [1], [2], [3], [4], [5] and [7] These techniques are inappropriate for identifying patterns that have a periodic behavior. One could analyze a transaction database and discover that a person typically purchases some items such as wine and cheese every weekend Finding such periodic patterns is useful for the purpose of marketing. This can result in finding a very large number of patterns, and it is typically difficult and time-consuming for users to analyze a large pattern set To address this issue, a novel problem is defined in this paper, which is to identify periodic patterns using three measures. The following sections describe relevant related work and some important preliminaries about frequent itemset mining (Sec. 2. ), present the designed minimum periodicity and average periodicity measures (Sec. 3. ), describe the proposed algorithm (Sec. 4. ), report results from the experimental evaluation (Sec. 5. ) and draw a conclusion and discusses opportunities for future work (Sec. 6. ), respectively
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
