Sequential Mining Research Articles

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

A survey of high utility sequential patterns mining methods

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given.

CCSMP: an efficient closed contiguous sequential pattern mining algorithm with a pattern relation graph

CCSMP: an efficient closed contiguous sequential pattern mining algorithm with a pattern relation graph

Evidence for an ancient aquatic origin of the RNA viral order Articulavirales.

The emergence of previously unknown disease-causing viruses in mammals is in part the result of a long-term evolutionary process. Reconstructing the deep phylogenetic histories of viruses helps identify major evolutionary transitions and contextualizes the emergence of viruses in new hosts. We used a combination of total RNA sequencing and transcriptome data mining to extend the diversity and evolutionary history of the RNA virus order Articulavirales, which includes the influenza viruses. We identified instances of Articulavirales in the invertebrate phylum Cnidaria (including corals), constituting a novel and divergent family that we provisionally named the "Cnidenomoviridae." We further extended the evolutionary history of the influenza virus lineage by identifying four divergent, fish-associated influenza-like viruses, thereby supporting the hypothesis that fish were among the first hosts of influenza viruses. In addition, we substantially expanded the phylogenetic diversity of quaranjaviruses and proposed that this genus be reclassified as a family-the "Quaranjaviridae." Within this putative family, we identified a novel arachnid-infecting genus, provisionally named "Cheliceravirus." Notably, we observed a close phylogenetic relationship between the Crustacea- and Chelicerata-infecting "Quaranjaviridae" that is inconsistent with virus-host codivergence. Together, these data suggest that the Articulavirales has evolved over at least 600 million years, first emerging in aquatic animals. Importantly, the evolution of the Articulavirales was likely shaped by multiple aquatic-terrestrial transitions and substantial host jumps, some of which are still observable today.

Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.

Applying Sequential Pattern Mining to Investigate the Temporal Relationships between Commonly Occurring Internal Medicine Diseases and Intervals for the Risk of Concurrent Disease in Canine Patients.

Sequential pattern mining (SPM) is a data mining technique used for identifying common association rules in multiple sequential datasets and patterns in ordered events. In this study, we aimed to identify the relationships between commonly occurring internal medicine diseases in canine patients. We obtained medical records of dogs referred to the Konkuk University Veterinary Medicine Teaching Hospital. The data used for SPM included comorbidities and intervals between the diagnoses of internal medicine diseases. Additionally, we estimated the 3-year risk of developing an additional disease after the initial diagnosis of a commonly occurring veterinary internal medicine disease using logistic regression. We identified 547 canine patients diagnosed with ≥ 1 internal medicine disease. The SPM-based analysis assessed comorbidities and intervals for each of the five most common internal medical diseases, including hyperadrenocorticism, myxomatous mitral valve disease, canine atopic dermatitis, chronic kidney disease, and chronic pancreatitis. The highest values of the association rule were 3.01%, 6.02%, 3.9%, 4.1%, and 4.84%, and the shortest intervals were 1.64, 13.14, 5.37, 17.02, and 1.7 days, respectively. This study proposes that SPM is an effective technique for identifying common associations and temporal relationships between internal medicine diseases, and can be used to assess the probability of additional admission due to the development of the subsequent disease that may be diagnosed in canine patients. The results of this study will help veterinarians suggest appropriate preventive measures or other medical treatments for canine patients with medical conditions that have not yet been diagnosed, but are likely to develop in the short term.

Targeted mining of contiguous sequential patterns

In recent years, sequential pattern mining (SPM) has been applied in many domains, including recommender systems, fraud detection, and other related industries. Target-oriented SPM (TSPM) has been proposed to improve the interpretability of SPM by addressing the issue that too many irrelevant or meaningless sequential patterns are often discovered, which facilitates data analysis. Nevertheless, current TSPM research does not consider contiguity to identify sequential patterns, despite the fact that it is an important feature of sequence data. Contiguity has significant implications in applications such as bioinformatics and network intrusion detection, as it can aid the detection of periodic patterns and anomalous behaviors. Therefore, this paper proposes a novel problem of target-oriented contiguous sequential pattern mining (TCSPM) and introduces two algorithms, called TCSPM and TCSPM+. TCSPM utilizes a compact data structure called sequence chain and relies on a sequence chain pruning strategy to reduce the search space. In addition, TCSPM applies a reverse matching technique to quickly search for targeted contiguous sequences. TCSPM+ is an optimized version of TCSPM that utilizes sequence segmentation operations and a query sequence pruning strategy to further improve performance. Extensive experiments on both real and synthetic datasets demonstrate that TCSPM is an efficient algorithm for discovering targeted contiguous sequential patterns (TCSPs). TCSPM+ substantially enhances the performance of TCSPM in terms of runtime, memory usage, and scalability, outperforming TCSPM by an order of magnitude on some datasets.

Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies varepsilon-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.

A Self-Attention Model for Next Location Prediction Based on Semantic Mining

With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to life patterns, and obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices are different from check-in data that have semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose a Self-Attention model for next location prediction based on semantic mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k-nearest POI, and then use the reciprocal of the distance from the stay point to the k-nearest POI and the number of categories as weights. Finally, we use the probability to express the semantic without losing other important semantic information. Furthermore, this research, combined with sequential pattern mining, can result in richer semantic features. In order to better perceive the trajectory, temporal features learn the periodicity of time series by the sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

Through the lens of artificial intelligence: A novel study of spherical video-based virtual reality usage in autism and neurotypical participants

The current study explores the use of computer vision and artificial intelligence (AI) methods for analyzing 360-degree spherical video-based virtual reality (SVVR) data. The study aimed to explore the potential of AI, computer vision, and machine learning methods (including entropy analysis, Markov chain analysis, and sequential pattern mining), in extracting salient information from SVVR video data. The research questions focused on differences and distinguishing characteristics of autistic and neurotypical usage characteristics in terms of behavior sequences, object associations, and common patterns, and the extent to which the predictability and variability of findings might distinguish the two participant groups and provide provisional insights into the dynamics of their usage behaviors. Findings from entropy analysis suggest the neurotypical group showed greater homogeneity and predictability, and the autistic group displayed significant heterogeneity and variability in behavior. Results from the Markov Chains analysis revealed distinct engagement patterns, with autistic participants exhibiting a wide range of transition probabilities, suggesting varied SVVR engagement strategies, and with the neurotypical group demonstrating more predictable behaviors. Sequential pattern mining results indicated that the autistic group engaged with a broader spectrum of classes within the SVVR environment, hinting at their attraction to a diverse set of stimuli. This research provides a preliminary foundation for future studies in this area, as well as practical implications for designing effective SVVR learning interventions for autistic individuals.

Retracted: Study on the Clinical Characteristics and Treatment of Obstetric Hypertension with Sequence Pattern Mining Algorithm.

[This retracts the article DOI: 10.1155/2021/1072446.].

A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.

An efficient pruning method for mining inter-sequence patterns based on pseudo-IDList

Mining inter-sequence patterns represents a relatively novel research direction, yielding significant contributions to the field of data mining research since its inception. Its versatile applications span various domains, with numerous research avenues emerging as a result. However, it is worth noting that inter-sequence mining algorithms tend to generate a substantial volume of frequent patterns. To date, research efforts in the realm of inter-sequence pattern mining have predominantly focused on creating, copying, and storing all candidate information. This approach has led to potential performance inefficiencies, necessitating more resources than may be inherently required. It is worth highlighting the recent introduction of the pseudo-IDList data structure, which has demonstrated promise in enhancing both runtime efficiency and memory utilization during mining tasks. Nevertheless, this innovation has thus far found application exclusively in clickstream mining and sequential pattern mining contexts.In this paper, we propose an extension of this data structure for the mining of frequent inter-sequence patterns and introduce a novel algorithm named Inter-Sequence Pattern mining based on Pseudo-Index. Additionally, our proposed algorithm incorporates a pruning method known as Inter-Sequence Pattern Mining with Index Intersection Checking, designed to effectively curtail the proliferation of generated candidates. Our experimental results unequivocally demonstrate that the proposed algorithm excels in processing time and storage space utilization when compared to existing state-of-the-art algorithms designed for mining inter-sequence patterns.

Study of the Impact of Hard Rock Pillar Behavior on the Raise Caving Mining System

Raise caving is a novel mining method with an active stress control approach, which is suitable for deep mining environments. The application of raise caving is investigated for LKAB’s Kiruna mine. This mining method consists of two different phases. In the de-stressing phase, inclined de-stressing slots are created with a minimum amount of infrastructure and long-raise bore holes. The de-stressing slots provide stress shadows for subsequent large scale mining activities. These slots are separated by massive pillars, with width to height ratios of up to 10 and a length of several hundreds of meters, which are required to control stresses and seismicity and prevent hangingwall caving. Due to the stress redistribution in the mine, pillars and abutment areas become highly stressed. In the so-called “production phase”, which follows the de-stressing phase, large scale mineral extraction is conducted. As pillars are highly stressed after the de-stressing phase, it is important to de-stress them in the production phase to be able to extract them in a safe manner. Overall, pillars play a decisive role for the success of the raise caving mining method. Hence, studies on pillars and their impact on the raise caving mining method are of great importance. The first part of the contribution outlines the importance of pillars in the raise caving mining method on a conceptual basis. Numerical simulations for different mine layouts and different pillar behavior are then conducted to study the impact of pillar behavior on the system. The stress redistribution in the mining system and the infrastructure stability, with the help of the RCF-value, are analyzed. For this purpose, simulations with a linear elastic material behavior are a well-suited method. Consequently, the de-stressing of the pillar due to crushing cannot be analyzed with a linear elastic model. To do so, a method for de-stressing the pillar has to be implemented into the simulation. The behavior for pillars with a different width to height ratio is implemented with the help of pre-calculated stress-strain curves from available knowledge on pillar strength and behavior. Since mining is a progressive process, the sequence of mining plays an important role for the stress development in pillars and abutment areas and is also included in the simulations. Different ways for pillar de-stressing are outlined and discussed. Furthermore, the influence of stoping in the production phase is discussed in this contribution.

Upregulated HAVCR2: A Prognostic and Immune-Related Marker in Testicular Germ Cell Tumors

Testicular germ cell tumors (TGCTs) are the most common solid malignant tumor in young men aged 20–34 years. Currently, the diagnosis and differentiation of TGCTs depend on immunohistochemical analysis, and the treatment methods are mainly surgery and chemoradiotherapy. Although immunotherapy has been applied in clinic, the response rate is not high, and also there is a lack of effective biomarkers. In this study, through high-throughput transcriptome sequencing and public data mining, the expression of hepatitis A virus cellular receptor 2 (HAVCR2) was found to be significantly upregulated in TGCT tissue and correlated with poor prognosis. Additionally, the expression level of HAVCR2 was negatively correlated with its DNA methylation but positively correlated with its copy number level. The HAVCR2 expression level was significantly positively correlated with the infiltration of six types of immune cells, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells. Also, the higher its expression level, the higher the immune cell abundance. Tumor immune dysfunction and exclusion (TIDE) scoring analysis showed that HAVCR2 was related to the responsiveness to TGCT immunotherapy. Drug sensitivity analysis revealed that HAVCR2 was related to the sensitivity of multiple antitumor drugs. This study demonstrated that HAVCR2 has high potential as a biomarker for the diagnosis, prognosis, and treatment of TGCTs.

Transferring effective learning strategies across learning contexts matters: A study in problem-based learning

Learning strategies are important catalysts of students’ learning. Research has shown that students with effective learning strategies are more likely to have better academic achievement. This study aimed to investigate students’ adoption of learning strategies in different course implementations, the transfer of learning strategies between courses and relationship to performance. We took advantage of recent advances in learning analytics methods, namely sequence and process mining as well as statistical methods and visualisations to study how students regulate their online learning through learning strategies. The study included 81,739 log traces of students’ learning related activities from two different problem-based learning medical courses. The results revealed that students who applied deep learning strategies were more likely to score high grades, and students who applied surface learning strategies were more likely to score lower grades in either course. More importantly, students who were able to transfer deep learning strategies or continue to use effective strategies between courses obtained higher scores, and were less likely to adopt surface strategies in the subsequent course. These results highlight the need for supporting the development of effective learning strategies in problem-based learning curricula so that students adopt and transfer effective strategies as they advance through the programme. Implications for practice or policy: Teachers need to help students develop and transfer deep learning as they are directly related to success. Students who continue to use light strategies are more at risk of low achievement and need to be supported. Technology-supported problem-based learning requires more active scaffolding and teachers’ support beyond “guide on the side” as in face-to-face.

MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner</uri> .

Automated gadget discovery in the quantum domain

In recent years, reinforcement learning (RL) has become increasingly successful in its application to the quantum domain and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent’s learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent’s policy.

Diversity and Pathobiology of an Ilarvirus Unexpectedly Detected in Diverse Plants and Global Sequencing Data.

High-throughput sequencing (HTS) and sequence mining tools revolutionized virus detection and discovery in recent years, and implementing them with classical plant virology techniques results in a powerful approach to characterize viruses. An example of a virus discovered through HTS is Solanum nigrum ilarvirus 1 (SnIV1) (Bromoviridae), which was recently reported in various solanaceous plants from France, Slovenia, Greece, and South Africa. It was likewise detected in grapevines (Vitaceae) and several Fabaceae and Rosaceae plant species. Such a diverse set of source organisms is atypical for ilarviruses, thus warranting further investigation. In this study, modern and classical virological tools were combined to accelerate the characterization of SnIV1. Through HTS-based virome surveys, mining of sequence read archive datasets, and a literature search, SnIV1 was further identified from diverse plant and non-plant sources globally. SnIV1 isolates showed relatively low variability compared with other phylogenetically related ilarviruses. Phylogenetic analyses showed a distinct basal clade of isolates from Europe, whereas the rest formed clades of mixed geographic origin. Furthermore, systemic infection of SnIV1 in Solanum villosum and its mechanical and graft transmissibility to solanaceous species were demonstrated. Near-identical SnIV1 genomes from the inoculum (S. villosum) and inoculated Nicotiana benthamiana were sequenced, thus partially fulfilling Koch's postulates. SnIV1 was shown to be seed-transmitted and potentially pollen-borne, has spherical virions, and possibly induces histopathological changes in infected N. benthamiana leaf tissues. Overall, this study provides information to better understand the diversity, global presence, and pathobiology of SnIV1; however, its possible emergence as a destructive pathogen remains uncertain.

Data Analytic for Healthcare Cyber Physical System

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data.