arXiv subjects

Wensheng Gan

Publications and source records attributed to Wensheng Gan.

At least 91 records · Page 5Linked to original sources

arXiv2022

Towards Revenue Maximization with Popular and Profitable Products

Economic-wise, a common goal for companies conducting marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Consumer behavior is crucially important in economy and targeted marketing, in which behavioral economics can provide valuable insights to identify the biases and profit from customers. Finding credible and reliable information on products' profitability is, however, quite difficult since most products tends to peak at certain times w.r.t. seasonal sales cycle in a year. On-Shelf Availability (OSA) plays a key factor for performance evaluation. Besides, staying ahead of hot product trends means we can increase marketing efforts without selling out the inventory. To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing. To tackle the revenue maximization problem, we model the k-satisfiable product concept and propose an algorithmic framework for searching OPPP and its variants. Extensive experiments are conducted on several real-world datasets to evaluate the effectiveness and efficiency of the proposed algorithm.

cs.AI↗

arXiv2022

TaSPM: Targeted Sequential Pattern Mining

Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.

cs.DB↗

arXiv2021

US-Rule: Discovering Utility-driven Sequential Rules

Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) was proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not enough efficient. In this paper, we propose a faster algorithm, called US-Rule, to efficiently mine high-utility sequential rules. It utilizes rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computation. To improve the efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, RERSU) and their corresponding pruning strategies (LEEUP, REEUP, LERSUP, RERSUP) are proposed. Besides, US-Rule proposes rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. At last, a large number of experiments on different datasets compared to the state-of-the-art algorithm demonstrate that US-Rule can achieve better performance in terms of execution time, memory consumption and scalability.

cs.DB↗

arXiv2021

Anomaly Rule Detection in Sequence Data

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.

cs.DB↗

arXiv2021

Flexible Pattern Discovery and Analysis

Based on the analysis of the proportion of utility in the supporting transactions used in the field of data mining, high utility-occupancy pattern mining (HUOPM) has recently attracted widespread attention. Unlike high-utility pattern mining (HUPM), which involves the enumeration of high-utility (e.g., profitable) patterns, HUOPM aims to find patterns representing a collection of existing transactions. In practical applications, however, not all patterns are used or valuable. For example, a pattern might contain too many items, that is, the pattern might be too specific and therefore lack value for users in real life. To achieve qualified patterns with a flexible length, we constrain the minimum and maximum lengths during the mining process and introduce a novel algorithm for the mining of flexible high utility-occupancy patterns. Our algorithm is referred to as HUOPM+. To ensure the flexibility of the patterns and tighten the upper bound of the utility-occupancy, a strategy called the length upper-bound (LUB) is presented to prune the search space. In addition, a utility-occupancy nested list (UO-nlist) and a frequency-utility-occupancy table (FUO-table) are employed to avoid multiple scans of the database. Evaluation results of the subsequent experiments confirm that the proposed algorithm can effectively control the length of the derived patterns, for both real-world and synthetic datasets. Moreover, it can decrease the execution time and memory consumption.

cs.DB↗

arXiv2021

Utility-driven Mining of Contiguous Sequences

Recently, contiguous sequential pattern mining (CSPM) gained interest as a research topic, due to its varied potential real-world applications, such as web log and biological sequence analysis. To date, studies on the CSPM problem remain in preliminary stages. Existing CSPM algorithms lack the efficiency to satisfy users' needs and can still be improved in terms of runtime and memory consumption. In addition, existing algorithms were developed to deal with simple sequence data, working with only one event at a time. Complex sequence data, which represent multiple events occurring simultaneously, are also commonly observed in real life. In this paper, we propose a novel algorithm, fast utility-driven contiguous sequential pattern mining (FUCPM), to address the CSPM problem. FUCPM adopts a compact sequence information list and instance chain structures to store the necessary information of the database and candidate patterns. For further efficiency, we develop the global unpromising items pruning and local unpromising items pruning strategies, based on sequence-weighted utilization and item-extension utilization, to reduce the search space. Extensive experiments on real-world and synthetic datasets demonstrate that FUCPM outperforms the state-of-the-art algorithms and is scalable enough to handle complex sequence data.

cs.DB↗

arXiv2021

FUIM: Fuzzy Utility Itemset Mining

Because of usefulness and comprehensibility, fuzzy data mining has been extensively studied and is an emerging topic in recent years. Compared with utility-driven itemset mining technologies, fuzzy utility mining not only takes utilities (e.g., profits) into account, but also considers quantities of items in each transaction for discovering high fuzzy utility itemsets (HFUIs). Thus, fuzziness can be regard as a key criterion to select high-utility itemsets, while the exiting algorithms are not efficient enough. In this paper, an efficient one-phase algorithm named Fuzzy-driven Utility Itemset Miner (FUIM) is proposed to find out a complete set of HFUIs effectively. In addition, a novel compact data structure named fuzzy-list keeps the key information from quantitative transaction databases. Using fuzzy-list, FUIM can discover HFUIs from transaction databases efficiently and effectively. Both completeness and correctness of the FUIM algorithm are proved by five theorems. At last, substantial experiments test three terms (runtime cost, memory consumption, and scalability) to confirm that FUIM considerably outperforms the state-of-the-art algorithms.

cs.DB↗

arXiv2021

TargetUM: Targeted High-Utility Itemset Querying

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of \textbf{TargetUM} is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.

cs.DB↗

arXiv2021

TOPIC: Top-k High-Utility Itemset Discovering

Utility-driven itemset mining is widely applied in many real-world scenarios. However, most algorithms do not work for itemsets with negative utilities. Several efficient algorithms for high-utility itemset (HUI) mining with negative utilities have been proposed. These algorithms can find complete HUIs with or without negative utilities. However, the major problem with these algorithms is how to select an appropriate minimum utility (minUtil) threshold. To address this issue, some efficient algorithms for extracting top-k HUIs have been proposed, where parameter k is the quantity of HUIs to be discovered. However, all of these algorithms can solve only one part of the above problem. In this paper, we present a method for TOP-k high-utility Itemset disCovering (TOPIC) with positive and negative utility values, which utilizes the advantages of the above algorithms. TOPIC adopts transaction merging and database projection techniques to reduce the database scanning cost, and utilizes minUtil threshold raising strategies. It also uses an array-based utility technique, which calculates the utility of itemsets and upper bounds in linear time. We conducted extensive experiments on several real and synthetic datasets, and the results showed that TOPIC outperforms state-of-the-art algorithm in terms of runtime, memory costs, and scalability.

cs.DB↗

arXiv2021

THUE: Discovering Top-K High Utility Episodes

Episode discovery from an event is a popular framework for data mining tasks and has many real-world applications. An episode is a partially ordered set of objects (e.g., item, node), and each object is associated with an event type. This episode can also be considered as a complex event sub-sequence. High-utility episode mining is an interesting utility-driven mining task in the real world. Traditional episode mining algorithms, by setting a threshold, usually return a huge episode that is neither intuitive nor saves time. In general, finding a suitable threshold in a pattern-mining algorithm is a trivial and time-consuming task. In this paper, we propose a novel algorithm, called Top-K High Utility Episode (THUE) mining within the complex event sequence, which redefines the previous mining task by obtaining the K highest episodes. We introduce several threshold-raising strategies and optimize the episode-weighted utilization upper bounds to speed up the mining process and effectively reduce the memory cost. Finally, the experimental results on both real-life and synthetic datasets reveal that the THUE algorithm can offer six to eight orders of magnitude running time performance improvement over the state-of-the-art algorithm and has low memory consumption.

cs.DB↗

arXiv2021

Explainable Fuzzy Utility Mining on Sequences

Fuzzy systems have good modeling capabilities in several data science scenarios, and can provide human-explainable intelligence models with explainability and interpretability. In contrast to transaction data, which have been extensively studied, sequence data are more common in real-life applications. To obtain a human-explainable data intelligence model for decision making, in this study, we investigate explainable fuzzy-theoretic utility mining on multi-sequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and thus expedite the mining process. Finally, the proposed PGFUM algorithm is compared with PFUS, which is the only currently available method for the same task, through extensive experimental evaluation. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.

cs.DB↗

arXiv2021

TUSQ: Targeted High-Utility Sequence Querying

Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.

cs.DB↗

arXiv2021

FRI-Miner: Fuzzy Rare Itemset Mining

Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases. Interesting itemset mining plays an important role in many real-life applications, such as market, e-commerce, finance, and medical treatment. To date, various data mining algorithms based on frequent patterns have been widely studied, but there are a few algorithms that focus on mining infrequent or rare patterns. In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications. In this paper, we introduce a novel fuzzy-based rare itemset mining algorithm called FRI-Miner, which discovers valuable and interesting fuzzy rare itemsets in a quantitative database by applying fuzzy theory with linguistic meaning. Additionally, FRI-Miner utilizes the fuzzy-list structure to store important information and applies several pruning strategies to reduce the search space. The experimental results show that the proposed FRI-Miner algorithm can discover fewer and more interesting itemsets by considering the quantitative value in reality. Moreover, it significantly outperforms state-of-the-art algorithms in terms of effectiveness (w.r.t. different types of derived patterns) and efficiency (w.r.t. running time and memory usage).

cs.DB↗

arXiv2020

On the Construction of a Post-Quantum Blockchain for Smart City

Owing to some special characteristics and features, blockchain is a very useful technique that can securely organize diverse devices in a smart city. It finds wide applications, especially in distributed environments, where entities such as wireless sensors need to be certain of the authenticity of the server. As contemporary blockchain techniques that address post-quantum concerns have not been designed, in this study, we investigate a blockchain in the post-quantum setting and seek to discover how it can resist attacks from quantum computing. In addition, traditional proof of work (PoW)-based consensus protocols such as Bitcoin cannot supply memory mining, and the transaction capacity of each block in a blockchain is limited and needs to be expanded. Thus, a new post-quantum proof of work (post-quantum PoW) consensus algorithm for security and privacy of smart city applications is proposed. It can be used to not only protect a blockchain under a quantum computing attack compared to existing classical hash-based PoW algorithms but also to supply memory mining. Meanwhile, an identity-based post-quantum signature is embedded into a transaction process to construct lightweight transactions. Subsequently, we provide a detailed description on the execution of the post-quantum lightweight transaction in a blockchain. Overall, this work can help enrich the research on future post-quantum blockchain and support the construction or architecture of emerging blockchain-based smart cities.

cs.CR↗

arXiv2020

TKUS: Mining Top-K High-Utility Sequential Patterns

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.

cs.DB↗

arXiv2020

On-shelf Utility Mining of Sequence Data

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this paper, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS+, to extract on-shelf high-utility sequential patterns. For further efficiency, we also designed several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility (TPEU) and time reduced sequence utility (TRSU). In addition, two novel data structures were developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS+ has wider real-life applications owing to its high efficiency.

cs.DB↗

arXiv2020

A Jumping Mining Attack and Solution

Mining is the important part of the blockchain used the proof of work (PoW) on its consensus, looking for the matching block through testing a number of hash calculations. In order to attract more hash computing power, the miner who finds the proper block can obtain some rewards. Actually, these hash calculations ensure that the data of the blockchain is not easily tampered. Thus, the incentive mechanism for mining affects the security of the blockchain directly. This paper presents an approach to attack against the difficulty adjustment algorithm (abbreviated as DAA) used in blockchain mining, which has a direct impact on miners' earnings. In this method, the attack miner jumps between different blockchains to get more benefits than the honest miner who keep mining on only one blockchain. We build a probabilistic model to simulate the time to obtain the next block at different hash computing power called hashrate. Based on this model, we analyze the DAAs of the major cryptocurrencies, including Bitcoin, Bitcoin Cash, Zcash, and Bitcoin Gold. We further verify the effectiveness of this attack called jumping mining through simulation experiments, and also get the characters for the attack in the public block data of Bitcoin Gold. Finally, we give an improved DAA scheme against this attack. Extensive experiments are provided to support the efficiency of our designed scheme.

cs.CR↗

arXiv2020

Discovering High Utility-Occupancy Patterns from Uncertain Data

It is widely known that there is a lot of useful information hidden in big data, leading to a new saying that "data is money." Thus, it is prevalent for individuals to mine crucial information for utilization in many real-world applications. In the past, studies have considered frequency. Unfortunately, doing so neglects other aspects, such as utility, interest, or risk. Thus, it is sensible to discover high-utility itemsets (HUIs) in transaction databases while utilizing not only the quantity but also the predefined utility. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high utility-occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High-Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by the algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). This algorithm divides user preferences into three factors, including support, probability, and utility occupancy. To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used, which assist in providing the downward closure property. Furthermore, an original tree structure, called support count tree (SC-tree), is constructed as the search space of the algorithm. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency.

cs.DB↗