Extracting recent weighted-based patterns from uncertain temporal databases. (May 2017)

• Record Type: Journal Article
• Authors: Gan, Wensheng; Lin, Jerry Chun-Wei; Fournier-Viger, Philippe; Chao, Han-Chieh; Wu, Jimmy Ming-Tai; Zhan, Justin
• Is Part Of: Engineering applications of artificial intelligence. Volume 61(2017:Jan.)
• Page Start: 161
• Page End: 172
• Publication Date: 2017-05
• Subjects: Weighted frequent itemset -- Recency constraint -- Uncertain data -- Upper bound -- SUBDC strategy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.03.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Weighted Frequent Itemset Mining (WFIM) has been proposed as an extension of frequent itemset mining that considers not only the frequency of items but also their relative importance. However, using WFIM algorithms in real applications raises some problems. First, they do not consider how recent the patterns are. Second, traditional WFIM algorithms cannot handle uncertain data, although this type of data is common in real-life. To address these limitations, this paper introduces the concept of Recent High Expected Weighted Itemset (RHEWI), which considers the recency, weight and uncertainty of patterns. By considering these three factors, more up-to-date and relevant results are found. A projection-based algorithm named RHEWI-P is presented to mine RHEWIs using a novel upper - bound downward closure ( UBDC ) property. An improved version of this algorithm called RHEWI-PS is further proposed based on a novel sorted upper-bound downward closure ( SUBDC ) property for pruning unpromising candidate itemsets early. An experimental evaluation against the state-of-the-art HEWI-Uapriori algorithm was carried out on both real-world and synthetic datasets. Results show that the proposed algorithms are highly efficient and are acceptable for mining the desired patterns.