Extracting recent weighted-based patterns from uncertain temporal databases
Document Type
Article
Publication Date
1-1-2017
Publication Title
Engineering Applications of Artificial Intelligence
Volume
61
First page number:
161
Last page number:
172
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. © 2017 Elsevier Ltd
Language
english
Repository Citation
Gan, W.,
Lin, J. C.,
Fournier Viger, P.,
Chao, H. C.,
Wu, J. M.,
Zhan, J.
(2017).
Extracting recent weighted-based patterns from uncertain temporal databases.
Engineering Applications of Artificial Intelligence, 61
161-172.
http://dx.doi.org/10.1016/j.engappai.2017.03.004