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Efficient methods for mining weighted clickstream patterns
(2020-03-15)
Huynh, Huy M.
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Nguyen, Loan T. T.
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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.
Mining Trending High Utility Itemsets from Temporal Transaction Databases
(2018-01-01)
Hackman, Acquah
;
Huang, Yu
;
Tseng, Vincent S.
;
交大名義發表
;
資訊工程學系
In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power of trend analytics. We introduce formal definitions for TrendHUI mining and highlighted the importance of the TrendHUI output. Moreover, we develop two algorithms, Two-Phase Trending High Utility Itemset (TP-THUI) miner and Two-Phase Trending High Utility Itemset Guided (TP-THUI-Guided) miner. Both are two-phase algorithms that mine a complete set of TrendHUI. TP-THUI-Guided miner utilizes a remainder utility to calculate the temporal trend of a given itemset to reduce the search space effectively, such that the execution efficiency can be enhanced substantially. Through a series of experiments, using three different datasets, the proposed algorithms prove to be excellent for validity and efficiency. To the best of our knowledge, this is the first work addressing the promising topic on Trending High Utility Itemset mining, which is expected to facilitate numerous applications in data mining fields.
Efficiently mining uncertain high-utility itemsets
(2017-06-01)
Lin, Jerry Chun-Wei
;
Gan, Wensheng
;
Fournier-Viger, Philippe
;
Hong, Tzung-Pei
;
Tseng, Vincent S.
Data mining consists of deriving implicit, potentially meaningful and useful knowledge from databases such as information about the most profitable items. High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms can only handle precise data, although big data collected in real-life applications using experimental measurements or noisy sensors is often uncertain. In this paper, an efficient algorithm, named Mining Uncertain High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) in uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mines PHUIs without generating candidates, and can avoid constructing PU-lists for numerous unpromising itemsets by applying several efficient pruning strategies, which greatly improve its performance. Extensive experiments conducted on both real-life and synthetic datasets show that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, and that the proposed MUHUI algorithm scales well when mining PHUIs in large-scale uncertain datasets.
Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease
(2017-03-01)
Cheng, Yi-Ting
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Lin, Yu-Feng
;
Chiang, Kuo-Hwa
;
Tseng, Vincent S.
;
資訊工程學系
Chronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining, and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling, and post analysis. For empiricasl evaluation, we demonstrate the effectiveness of our proposed framework with a case study on early assessment of COPD. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach can not only derive rich sequential risk patterns but also extract novel patterns with valuable insights for further medical investigation such as discovering novel markers and better treatments. To the best of our knowledge, this is the first work addressing the issue of mining sequential risk patterns with time-intervals as well as classification models for early assessment of chronic diseases.
Detecting exact breakpoints of deletions with diversity in hepatitis B viral genomic DNA from next-generation sequencing data
(2017-10-01)
Cheng, Ji-Hong
;
Liu, Wen-Chun
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Chang, Ting-Tsung
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Hsieh, Sun-Yuan
;
Tseng, Vincent S.
Many studies have suggested that deletions of Hepatitis B Viral (HBV) are associated with the development of progressive liver diseases, even ultimately resulting in hepatocellular carcinoma (HCC). Among the methods for detecting deletions from next-generation sequencing (NGS) data, few methods considered the characteristics of virus, such as high evolution rates and high divergence among the different HBV genomes. Sequencing high divergence HBV genome sequences using the NGS technology outputs millions of reads. Thus, detecting exact breakpoints of deletions from these big and complex data incurs very high computational cost. We proposed a novel analytical method named VirDelect (Virus Deletion Detect), which uses split read alignment base to detect exact breakpoint and diversity variable to consider high divergence in single-end reads data, such that the computational cost can be reduced without losing accuracy. We use four simulated reads datasets and two real pair-end reads datasets of HBV genome sequence to verify VirDelect accuracy by score functions. The experimental results show that VirDelect outperforms the state-of-the-art method Pindel in terms of accuracy score for all simulated datasets and VirDelect had only two base errors even in real datasets. VirDelect is also shown to deliver high accuracy in analyzing the single-end read data as well as pair-end data. VirDelect can serve as an effective and efficient bioinformatics tool for physiologists with high accuracy and efficient performance and applicable to further analysis with characteristics similar to HBV on genome length and high divergence. The software program of VirDelect can be downloaded at https://sourceforge.net/projects/virdelect/. (C) 2017 Published by Elsevier Inc.
Weighted frequent itemset mining over uncertain databases
(2016-01)
Lin, Jerry Chun-Wei
;
Gan, Wensheng
;
Fournier-Viger, Philippe
;
Hong, Tzung-Pei
;
Tseng, Vincent S.
Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining.
Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints
(2016-10)
Lu, Eric Hsueh-Chan
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Fang, Shih-Hsin
;
Tseng, Vincent S.
;
資訊工程學系
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Department of Computer Science
In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner (PAT-Planner) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users\' travel constraints. In PAT-Planner, we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening, based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.
Efficient Mining of Uncertain Data for High-Utility Itemsets
(2016)
Lin, Jerry Chun-Wei
;
Gan, Wensheng
;
Fournier-Viger, Philippe
;
Hong, Tzung-Pei
;
Tseng, Vincent S.
High-utility itemset mining (HUIM) is emerging as an important research topic in data mining. Most algorithms for HUIM can only handle precise data, however, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) from uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mine PHUIs without candidate generation and can reduce the construction of PU-lists for numerous unpromising itemsets by using several efficient pruning strategies, thus greatly improving the mining performance. Extensive experiments both on real-life and synthetic datasets proved that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, especially, the MUHUI algorithm scales well on large-scale uncertain datasets for mining PHUIs.
Weighted frequent itemset mining over uncertain databases
(2016-01-01)
Lin, Jerry Chun-Wei
;
Gan, Wensheng
;
Fournier-Viger, Philippe
;
Hong, Tzung-Pei
;
Tseng, Vincent S.
Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining.

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