Sequential Pattern Mining Approach Research Articles

A Sequential Pattern Mining Approach to Tourist Movement: The Case of a Mega Event

The movement of tourists has important economic and social implications for destination management. However, tracking and analyzing such movement can be a challenge both conceptually and methodologically. Using four different sequential pattern mining algorithms, this study investigates the movement of international visitors during the Gold Coast Commonwealth Games (GC2018) at the level of a specific destination through Twitter data. Results indicate that sequential pattern mining is a powerful technique to reveal complex travel patterns and provides insights into the potential associated destinations of visitors beyond the current point-to-point analysis. This approach can assist destination management and event organizers in identifying the event’s contribution to tourists’ local visitation.

Sequential Pattern Mining Approach for Personalized Fraudulent Transaction Detection in Online Banking

Financial institutions face challenges of fraud due to an increased number of online transactions and sophisticated fraud techniques. Although fraud detection systems have been implemented to detect fraudulent transactions in online banking, many systems just use conventional rule-based approaches. Rule-based detection systems have a difficulty in updating and managing their rules and conditions manually. Additionally, generated from the few fraud cases, the rules are general rather than specific to each user. In this paper, we propose a personalized alarm model to detect frauds in online banking transactions using sequence pattern mining on each user’s normal transaction log. We assumed that a personalized fraud detection model is more effective in responding to the rapid increase in online banking users and diversified fraud patterns. Moreover, we focused on the fact that fraudulent transactions are very different from each user’s usual transactions. Our proposed model divides each user’s log into transactions, extracts a set of sequence patterns, and uses it to determine whether a new incoming transaction is fraudulent. The incoming transaction is divided into multiple windows, and if the normal patterns are not found in the consecutive windows, an alarm is sounded. We applied the model to a real-world dataset and showed that our model outperforms the rule-based model and the Markov chain model. Although more experiments on additional datasets are needed, our personalized alarm model can be applied to real-world systems.

Conversion paths of online consumers: A sequential pattern mining approach

As the number and variety of online channels increase, businesses and advertisers must realize efficient marketing strategies and marketing mix. Customers' online channel usage patterns can be heterogeneous depending on various variables. If they understand this behavior correctly and use them for channel inflow or advertisement toward customers, they can establish an effective marketing strategy. In this paper, we utilize sequential pattern mining to find homogeneous patterns in customer's conversion paths by using conversion path data of customers who purchase diapers and find related insights. Furthermore, by analyzing the customer's channel movement pattern by dividing it into demographic information, we examine how demographic factors affect customer's online channel usage behavior.

KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies within manufacturing processes. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. This brings the semantic gap issue which stands for the lack of interoperability among different manufacturing systems. Furthermore, as the Cyber-Physical Systems (CPS) are becoming more knowledge-intensive, uniform knowledge representation of physical resources and real-time reasoning capabilities for analytic tasks are needed to automate the decision-making processes for these systems. These requirements highlight the potential of using symbolic AI for predictive maintenance. To automate and facilitate predictive analytics in Industry 4.0, in this paper, we present a novel Knowledge-based System for Predictive Maintenance in Industry 4.0 (KSPMI). KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The hybrid approach involves using statistical AI technologies such as machine learning and chronicle mining (a special type of sequential pattern mining approach) to extract machine degradation models from industrial data. On the other hand, symbolic AI technologies, especially domain ontologies and logic rules, will use the extracted chronicle patterns to query and reason on system input data with rich domain and contextual knowledge. This hybrid approach uses Semantic Web Rule Language (SWRL) rules generated from chronicle patterns together with domain ontologies to perform ontology reasoning, which enables the automatic detection of machinery anomalies and the prediction of future events’ occurrence. KSPMI is evaluated and tested on both real-world and synthetic data sets.

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences.

Educational data mining research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement.

Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach

Public bicycles are an indispensable part of green public transportation and are also a convenient and economical manner for the general public. In operation management, it is very important and imperative to understand the user demand and pattern of the public bicycle system. This paper took the public bicycle system in Hohhot as the research object, collected nearly 4 years of operating data, and studied the travel preferences of users in the public bicycle system in view of multiple granularities. Specifically, the data of car rental users at three time-granularities were obtained through data extraction technology. Finally, frequent pattern mining was performed on car rental data based on different time granularities and mapped to the user’s riding preference, and then the riding modes of different car rental users founded on different time granularities were determined. Finally, this article gave different management opinions based on the different riding preferences of public bicycle users in Hohhot.

Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns

Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.

Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach.

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events' prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.

Towards alignment strategies in human-agent interactions based on measures of lexical repetitions

Alignment of communicative behaviour is an important feature of Human–Human interaction that directly affects the collaboration and the social connection of conversational partners. With the aim of improving the communicative abilities of a virtual agent, and in particular its strategies related to (lexical) verbal alignment, this article focuses on the alignment of linguistic productions of dialogue participants in task-oriented dialogues. We propose a new framework to quantify both the lexical alignment and the self-repetition behaviours of dialogue participants from dyadic dialogue transcripts. The framework involves easily computable measures based on repetition of lexical patterns automatically extracted via a sequential pattern mining approach. These measures allow the characterisation of the nature of these processes by addressing various informative aspects such as their variety, complexity, and strength. This framework is implemented in the freely available and open-source software dialign. Using these measures, we present a contrastive study between Human–Human and Human–Agent dialogues on various corpora that reveals major differences in the lexical alignment and self-repetition behaviours. Lastly, we address the challenge of integrating lexical alignment capabilities in artificial agents. To this end, we describe guidelines and we discuss the integration of the proposed framework in a real-time dialogue system.

A two-phase sequential pattern mining framework to detect stealthy P2P botnets

The botnet has been one of the most common threats to network security. Among all emerged botnet, Peer to Peer (P2P) botnets are more perilous and resistant due to their distributed nature. In addition to their resiliency against takedown strategies, modern P2P botnets are stealthier in the way they perform fraudulent activities. One of the main challenges to detect P2P bots/botnets is the presence of benign P2P traffic. The botnet traffic can blend in with legitimate P2P traffic, and it makes the P2P bots stealthier. However, the problem of detecting P2P botnets in the presence of legitimate P2P traffic has received little attention from the research community.In this paper, a novel P2P botnet detection framework resilient to the presence of legitimate P2P traffic is proposed based on a two-phase Sequential Pattern Mining (SPM) approach. The proposed framework is evaluated in many different cases of the coexistence of P2P malicious and legitimate traffics, using real-world network traffic. Our experimental results show that the proposed framework is capable of detecting P2P bots in the presence of legitimate P2P traffic with a detection rate of 99.2%. Besides its accurate detection, our proposed framework is highly scalable and can detect even one bot in the network or different bots from different bot families.

Cascading Failure Pattern Identification in Power Systems Based on Sequential Pattern Mining

Cascading failure simulation data contain many fault chains (FCs), which can present cascading failure propagation paths. In cascading failure process, some component outages play dominating roles in propagation, indicating the common characteristics among different FCs. The commonness is embodied as combinations of components which are vulnerable to trip and cause serious blackout consequences. In addition, a component outage will increase the outage probability of relevant components and induce dependent outage in subsequent stage. Such relevance between two components can be called component outage causality. A combination of sequential component outages with outage causalities, which exists in different FCs and leads to system load loss, can be regarded as a cascading failure pattern (CFP). Statistical characteristics of FCs indicate that CFPs are variously distributed in different FCs, can present propagation paths and cause different impacts on system blackouts. This paper proposes a cascading failure pattern (CFP) identification method based on sequential pattern mining approach. The proposed method focuses on mining CFPs from massive FCs, quantifies the influence of CFP on system blackout and identifies the critical ones. The proposed method is verified with FCs data on IEEE 39-bus and 118-bus test systems.

Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale. Its use began with processing items baskets to learn patterns and have a directed marketing strategy but it is generalized to telecommunication alarms management with several works. Our work is in line with this, as it tries to locate patterns and identify them to make predictive statements about certain patterns. It is axed around providing a way to break sequences into episodes and assigning them a value of confidence and support, more precisely in the discovery of frequent patterns of episodes within a time window. Experimental results have shown the effectiveness of our sequential pattern mining approach and its adaptability to alarm management and analytics.

Integrated Probabilistic Data Structure For Accurate and Scalable Sequence Prediction

The inevitable success of predictive analytics lies in the prospect of cutting edge approaches proposed in the domain of data mining and machine learning. Frequent itemsets, sequential pattern mining has been the major sub domains of the data mining since its inception with human work. Sequence prediction is a rather more targeted approach towards the predictive data mining. Sequential pattern mining approaches, while being very efficient in finding associations and interactions of difference itemsets and discovering hidden pattern in the transactional or graph datasets, sequence prediction is better focused on predicting the next item of an existent sequence. Applications of sequence prediction has been in pattern prediction, stock value prediction, websites prefetching, fraud detection, breach prediction, text prediction, product recommendation in extensive manner. Several state-of-the-art algorithms and models have been proposed starting from graphical models, markovian models and tree based structures have been proposed by data mining. Machine learning models like recurrent neural networks with LSTM blocks work pretty well in terms of performance. Our aim is to propose an integrated data structure with better performance measures (space-time efficiency, scalability) and which can be effectively used in online learning setting-stream processing algorithm. The paper also describes the extension prospect of this algorithm which can be used in the time-series forecast.

A sequential pattern mining approach to identifying potential areas for business diversification

ABSTRACTAlthough many quantitative models have been presented to identify potential areas for business diversification, most have focused on the assessment of technological capabilities and/or similarities using patent information. New data sources and scientific methods have thus seldom been addressed. We propose a sequential pattern mining approach to identifying potential areas for business diversification using the historical business segment data. Our approach includes (1) sequential pattern mining to identify potential areas for business diversification by extracting the significant changing patterns of firms’ business segments; and (2) index analysis to assess the market and financial characteristics of the areas identified. Taken together, three diversification strategy maps are developed to provide comprehensive views of analysis results. An empirical analysis of 25,126 unique firms with 1320 business segments confirms that the proposed approach enables a wide-ranging search for potential areas for business diversification and the quick assessment of their characteristics.

A Survey of Parallel Sequential Pattern Mining

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

An application framework for mining online learning processes through event-logs

PurposeLearning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS.Design/methodology/approachThe paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations.FindingsThe analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones.Originality/valueThe study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

Purchase Recommendation and Product Inventory Management using Content Based Filtering with Sequential Pattern Mining Approach

Today, the product sales at XYZ Bookstore are increase in accordance to the trend in society. In that case, high sales must be supported by good supply and on target. Product sold based on needs of consumers will make possibility to achieve high sales. Using the Sequential Pattern Mining approach, we can specify sales patterns of products in relation to another products. SPADE (Sequential Pattern Discovery using Equivalence classes) is an algorithm that can be used to find sequential patterns in a large database. This algorithm finds frequent sequences of the sales transaction data using database vertical and join process of the sequence. The results of SPADE algorithm is frequent sequences which are used to form the rules. Those can be used as predictors of other items that will be purchased by consumers in the future. The result of this study is a lot of unique sequence appears that can provide the best advice for Merchandiser Officer, for example, there are 1.468 sequences that prove the customer who bought the product in Children’s Book category will always bought the same thing in the others day. This research produce some recommendation, one of the recommendation is Children's Book category has a very high chance of being a Best Seller for a long time so that the purchasing officer on XYZ bookstore should ensure that the product's supply of the category is always safe throughout the year. It means SPADE is successfully used to provide the advice and Merchandiser Officer must ensure the stock of that product is always available to avoid Lost Sales.

An empirical study of using sequential behavior pattern mining approach to predict learning styles

The learning style of a learner is an important parameter in his learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments to increase learners’ performance. Thus, it is important to be able to automatically determine learning styles of learners in an e-learning environment. In this paper, we propose a sequential pattern mining approach to extract frequent sequential behavior patterns, which can separate learners with different learning styles. In this research, in order to recognize learners’ learning styles, system uses the Myers-Briggs Type Indicator’s (MBTI). The approach has been implemented and tested in an e-learning environment and the results show that learning styles of learners can be predicted with high accuracy. We show that learners with similar learning styles have similar sequential behavior patterns in interaction with an e-learning environment. A lot of frequent sequential behavior patterns were extracted which some of them have a meaningful relation with MBTI dimensions.

Relevance- and Frequency-Enabled Trip Planning Model Based on Socio-economic Status

AbstractPlanning a trip not only depends on the traveling cost, time, and path, but also on the socio-economic status of the traveler. This paper attempts to introduce a new trip planning model that is able to work on real-time data with multiple socio-economic constraints. The proposed trip planning model processes real-time data to extract the relevant socio-economic attributes; later, it mines the most frequent as well as the feasible attributes to plan the trip. The relevance of the socio-economic constraints is defined using correlations, whereas the frequent as well as the feasible attributes are mined through the sequential pattern mining approach. Real-time travel information of about 38,303 trips was acquired from the Indian city of Hyderabad, and the proposed model was subjected to experimentation. The proposed model maintained a substantial trade-off between multiple performance metrics, though the trip mean model performed statistically.

A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach

Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.