Browsing by Author "Vo, Bay"

Now showing 1 - 2 of 2

Efficient methods for mining weighted clickstream patterns
(2020-03-15)
Huynh, Huy M.
;
Nguyen, Loan T. T.
;
Vo, Bay
;
Anh Nguyen
;
Tseng, Vincent S.
Pattern mining has been an attractive topic for many researchers since its first introduction. Clickstream mining, a specific version of sequential pattern mining, has been shown to be important in the age of the Internet. However, most previous works have simply exploited and applied existing sequential pattern algorithms to the mining of clickstream patterns, and few have studied clickstreams with weights, which also have a wide range of application. In this paper, we address this problem by proposing an approach based on the average weight measure for clickstream pattern mining and adapting a previous state-of-the-art algorithm to deal with the problem of weighted clickstream pattern mining. Following this, we propose an improved method named Compact-SPADE to enhance both the efficiency and memory consumption. Through various tests on both real-life and synthetic databases, we show that our proposed algorithms outperform state-of-the-art alternatives in terms of efficiency, memory requirements and scalability. (C) 2019 Elsevier Ltd. All rights reserved.
Mining high-utility itemsets in dynamic profit databases
(2019-07-01)
Nguyen, Loan T. T.
;
Phuc Nguyen
;
Nguyen, Trinh D. D.
;
Vo, Bay
;
Fournier-Viger, Philippe
High-Utility Itemset (HUI) mining is an important data-mining task which has gained popularity in recent years due to its applications in numerous fields. HUI mining aims at discovering itemsets that have high utility (e.g., yield a high profit) in transactional databases. Although several algorithms have been designed to enumerate all Mils, an important issue is that they assume that the utilities (e.g., unit profits) of items are static. But this simplifying assumption does not hold in real-life situations. For example, the unit profits of items often vary over time in a retail store due to fluctuating supply costs and promotions. Ignoring this important characteristic of real-life transactional databases makes current HUI-mining algorithms inapplicable in many real-world applications. To address this critical limitation of current HUI-mining techniques, this paper studies the novel problem of mining HUls in databases having dynamic unit profits. To accurately assess the utility of any itemset in this context, a redefined utility measure is introduced. Furthermore, a novel algorithm named MEFIM (Modified EFficient high-utility Itemset Mining), which relies on a novel compact database format to discover the desired itemsets efficiently, is designed. An improved version of the MEFIM algorithm, named iMEFIM, is also introduced. This algorithm employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on dynamic profit databases in terms of runtime, memory usage, and scalability. (C) 2019 Elsevier B.V. All rights reserved.