Pattern Mining Research Articles

Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis.

Numerous investigations of the spatiotemporal patterns of infectious disease epidemics, their potential influences, and their driving mechanisms have greatly contributed to effective interventions in the recent years of increasing pandemic situations. However, systematic reviews of the spatiotemporal patterns of communicable diseases are rare. Using bibliometric analysis, combined with content analysis, this study aimed to summarize the number of publications and trends, the spectrum of infectious diseases, major research directions and data-methodological-theoretical characteristics, and academic communities in this field. Based on 851 relevant publications from the Web of Science core database, from January 1991 to September 2021, the study found that the increasing number of publications and the changes in the disease spectrum have been accompanied by serious outbreaks and pandemics over the past 30 years. Owing to the current pandemic of new, infectious diseases (e.g., COVID-19) and the ravages of old infectious diseases (e.g., dengue and influenza), illustrated by the disease spectrum, the number of publications in this field would continue to rise. Three logically rigorous research directions-the detection of spatiotemporal patterns, identification of potential influencing factors, and risk prediction and simulation-support the research paradigm framework in this field. The role of human mobility in the transmission of insect-borne infectious diseases (e.g., dengue) and scale effects must be extensively studied in the future. Developed countries, such as the USA and England, have stronger leadership in the field. Therefore, much more effort must be made by developing countries, such as China, to improve their contribution and role in international academic collaborations.

Timed pattern-based analysis of collaboration failures in system-of-systems

A system-of-systems (SoS) tries to achieve prominent goals, such as increasing road capacity in platooning that groups driving vehicles in proximity, through interactions between constituent systems (CSs). However, during the collaboration of CSs, unintended interference in interactions causes collaboration failures that may lead to catastrophic damage, particularly for the safety–critical SoS. It is necessary to analyze the failure-inducing interactions (FIIs) during the collaboration and resolve the root causes of failures. Existing studies have utilized pattern-mining techniques to analyze system failures from logs. However, they have three limitations when applied to collaboration failures: (1) information loss caused by the limited capabilities of handling interaction logs; (2) limitations in identifying multiple failure patterns in a log; (3) absence of an end-to-end solution mapping patterns to faults. To overcome these limitations, we propose an FII pattern mining algorithm covering the main features of SoS interaction logs, an overlapping clustering technique for multiple pattern mining, and a pattern-based fault localization method. In experiments conducted on platooning and mass casualty incident-response SoS, the proposed approach exhibited the highest pattern mining and clustering accuracy and achieved feasible localization performance compared with existing methods. The findings of this study can facilitate the accurate analysis of collaboration failures.

Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.

Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.

CFGM: An algorithm for closed frequent graph patterns mining

The extraction of frequent subgraphs is a basic and well studied operation on graphs. Thus, mining frequent graph patterns and problems associated with it is very important. However, the number of frequent subgraphs is potentially exponential while mining large graph patterns. This issue can be partly overcome using closed frequent graphs mining. Instead of mining all frequent subgraphs, it is more efficient to enumerate only the closed frequent graphs. Thus, in this paper, we propose a novel closed frequent subgraph mining algorithm: CFGM. In this algorithm, a stack-based architecture is used to enumerate the frequent graph represented by the depth-first search. Moreover, this algorithm defines a strict partial order among frequent graphs. We demonstrate that, with respect to this strict partial order, only maximal elements (frequent graphs) need to be discovered. A pruning strategy is developed based on this strict partial order that dramatically reduces unnecessary frequent subgraphs to be enumerated. Computational results show our algorithm displays excellent performance, especially for some large asymmetric frequent graph patterns.

TKIFRPM: A Novel Approach for Topmost-K Identical Frequent Regular Patterns Mining from Incremental Datasets

The regular frequent pattern mining (RFPM) approaches are aimed to discover the itemsets with significant frequency and regular occurrence behavior in a dataset. However, these approaches mainly suffer from the following two issues: (1) setting the frequency threshold parameter for the discovery of regular frequent patterns technique is not an easy task because of its dependency on the characteristics of a dataset, and (2) RFPM approaches are designed to mine patterns from the static datasets and are not able to mine dynamic datasets. This paper aims to solve these two issues by proposing a novel top-K identical frequent regular patterns mining (TKIFRPM) approach to function on online datasets. The TKIFRPM maintains a novel synopsis data structure with item support index tables (ISI-tables) to keep summarized information about online committed transactions and dataset updates. The mining operation can discover top-K regular frequent patterns from online data stored in the ISI-tables. The TKIFRPM explores the search space in recursive depth-first order and applies a novel progressive node’s sub-tree pruning strategy to rapidly eliminate a complete infrequent sub-tree from the search space. The TKIFRPM is compared with the MTKPP approach, and it found that it outperforms its counterpart in terms of runtime and memory usage to produce designated topmost-K frequent regular pattern mining on the datasets following incremental updates.

Hybrid Recommender System for Mental Illness Detection in Social Media Using Deep Learning Techniques.

Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database's frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure's efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class. This will mitigate the feature vector's dimensionality by eliminating redundant, irrelevant, or noisy data. This work presents a new hybrid recommender system based on optimized feature selection and systolic tree. The features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), feature selection with the utilization of River Formation Dynamics (RFD), and the Particle Swarm Optimization (PSO) algorithm. The systolic tree is used for pattern mining, and based on this, the recommendations are given. The proposed methods were evaluated using the MovieLens dataset, and the experimental outcomes confirmed the efficiency of the techniques. It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborative filtering, the precision of 0.89 was achieved.

Mining Interesting Negative Sequential Patterns Based on Influence

Negative sequential pattern (NSP) mining can capture frequently occurring and non-occurring behavior information and can play an irreplaceable role in many applications. Most traditional NSP mining algorithms adopt a support measure to discover interesting patterns. However, the support measure does not truly reflect the interestingness of patterns in some cases. In particular, it ignores the effect of the support of every element and the order characteristics among these elements. Hence, an influence measure was proposed to truly reflect the interestingness of patterns. However, the current influence measure is used only in positive sequential pattern (PSP) mining and does not involve NSPs. To address these problems, this study proposes an algorithm, InfI-NSP, to mine interesting NSPs based on influence. First, we modify an existing NSP mining algorithm to efficiently mine NSPs. Second, we modify the influence measure and apply it to NSP mining to mine interesting NSPs. To the best of our knowledge, InfI-NSP is the first algorithm to mine interesting NSPs based on influence. Experiments on real-life and synthetic datasets show that InfI-NSP is effective.

A Utility-Based Distributed Pattern Mining Algorithm With Reduced Shuffle Overhead

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability.

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three new measures are defined: the utility, high support, and high-utility period sequence ratios. Further, a new upper bound, , and two new pruning properties were proposed. MHUPFPS uses a newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improve the overall performance of the algorithm. Furthermore, the proposed algorithm is evaluated using several datasets. The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utility periodic frequent patterns.

Heart failure disease prediction and stratification with temporal electronic health records data using patient representation

Accurate early prediction of heart failure and identification of heart failure sub-phenotypes can enable in-time interventions and treatments, assist with policy decisions, and lead to a better understanding of disease pathophysiology in groups of patients. However, decision making more challenging for clinicians since the available data is complex, heterogeneous, temporal, and different in granularity. Even with much data, it is difficult for a cardiologist to pre-judge a patient’s heart condition at the next visit by relying on data from only one visit. Moreover, complicated and overloaded information bewilders clinicians, bringing obstacles to the stratification of patients and the mining of disease typical patterns in subgroups. To overcome these issues, this study proposes a novel Patient Representation model based on a temporal Bidirectional neural network with an Attention mechanism deep learning model called tBNA-PR. tBNA-PR effectively models heterogeneous and temporal Electronic Health Records (tEHRs) data from past and future directions to obtain informative patient representation to realize accurate heart failure prediction and reasonable patient stratification. Additionally, this study extracts typical diagnosis and prescriptions for disease patterns exploration and identifies significant features of sub-phenotypes for subgroup explanation in the context of complex clinical settings to provide better quality healthcare services and clinical decision support. This study leverages a real-world dataset MIMIC-III database. We carried out experiments on the prediction of heart failure to investigate tBNA-PR, which obtains prediction accuracy of 0.78, F1-Score of 0.7671, and AUC of 0.7198, showing a certain superiority compared with several state-of-the-art benchmarks. Moreover, we identified three distinct sub-phenotypes in all heart failure patients in the dataset with the clustering method and subgroup analysis. Sub-phenotype I has characteristics of more long-term anticoagulants. This sub-group has more patients who have the thrombotic disease. Sub-phenotype II has features of more patients having kidney disease, pneumonia, urinary tract infection, and coronary heart disease surgery history. Sub-phenotype III has characteristics of more patients having acidosis, depressive disorder, esophageal reflux, obstructive sleep apnea, and acquired hypothyroidism. Statistical tests show that the features, including age, creatinine, hemoglobin, urea nitrogen, and blood potassium, are significantly different among the three sub-phenotypes and have particular high importance. The resultant findings from this work have practical implications for clinical decision support.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

An Effective Method for Mining Negative Sequential Patterns From Data Streams

Traditional negative sequential patterns(NSPs) mining algorithms are used to mine static dataset which are stored in equipment and can be scanned many times. Nowadays, with the development of technology, many applications produce a large amount of data at a very high speed, which is called as data stream. Unlike static data, data stream is transient and can usually be read only once. So, traditional NSP mining algorithm cannot be directly applied to data stream. Briefly, the key reasons are: (1) inefficient negative sequential candidates generation method, (2) one-time mining, (3) lack of real-time processing. To solve this problem, this paper proposed a new algorithm mining NSP from data stream, called nsp-DS. First, we present a method to generate positive and negative sequential candidates simultaneously, and a new negative containment definition. Second, we use a sliding window to store sample data in current time. The continuous mining of entire data stream is realized through the continuous replacement of old and new data. Finally, a prefix tree structure is introduced to store sequential patterns. Whenever the user requests, it traverses the prefix tree to output sequential patterns. The experimental results show that nsp-DS may discover NSPs from data streams.

PatCluster: A Top-Down Log Parsing Method Based on Frequent Words

Logs are a combination of static message type fields and dynamic variable fields, and the accuracy of log parsing affects the result of subsequent log analysis tasks. In this regard, an offline log parsing method based on frequent words is introduced: PatCluster. This method first generates root nodes by preprocessing; secondly, the frequency of words is counted, and the word with the largest frequency is extracted as the segmentation condition to refine the template generated by the root node. So on recursively, pattern nodes are formed for all elements of the nodes, and corresponding templates are generated to finally achieve the purpose of log pattern mining. The mining process of the log patterns is from coarse to fine which is based on fewer assumptions, and the pattern fitting depth can be controlled by adjusting the termination condition. In optimized algorithm model, we also consider the maximum extent of the log template matching the token in the log message. The experimental results show that this method effectively improves the log parsing quality and has higher log parsing accuracy than other methods, and is more suitable for handling logs with complex structures.

Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system

This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items? cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.

Context-Aware and Click Session-Based Graph Pattern Mining With Recommendations for Smart EMS Through AI

In the field of Artificial Intelligence (AI), Smart Enterprise Management Systems (Smart EMS) and big data analytics are the most prominent computing technologies. A key component of the Smart EMS system is E-commerce, especially Session-based Recommender systems (SRS), which are typically utilized to enhance the user experience by providing recommendations analyzing user behavior encoded in browser sessions. Also the work of the recommender is to predict users’ next actions (click on an item) using the sequence of actions in the current session. Current developments in session-based recommendation have primarily focused on mining more information accessible within the current session. On the other hand, those approaches ignored sessions with identical context for the current session that includes a wealth of collaborative data. Therefore this paper proposed Context-aware and Click session-based graph pattern mining with recommendations for Smart EMS through AI. It employs a novel Triple Attentive Neural Network (TANN) for SRS. Specifically, TANN contains three main components, i.e., Enhanced Sqrt-Cosine Similarity based Neighborhood Sessions Discovery (NSD), Frequent Subgraph Mining (FSM) using Neighborhood Click session-based graph pattern mining and Top-K possible Next-clicked Items Discovery (TNID). The NSD module uses a session-level attention mechanism to find m most similar sessions of the query session, and the FSM module also extracts the frequent subgraphs from the already discovered m most similar sessions of the query session via item-level attention. Then, TNID module is used to discover the top-K possible next-clicked items using the NSD and FSM module via a target-level attention. Finally, we perform comprehensive experiments on one big dataset, DIGINETICA, to verify the effectiveness of the TANN model, and the results of this experiment clearly illustrate the performance of TANN.

Design of an English Web-based Teaching Resource Sharing Platform based on Mobile Web Technology

Thanks to the booming technology of computers and multimedia, student-centered online teaching resource platforms have become an important way for students to learn. However, English teaching resource platforms at the present stage fail to effectively integrate the massive and scattered learning resources. Based on this, the study proposes an English online teaching resource sharing platform based on mobile Web technology, using the SOAP protocol to deploy heterogeneous data resources as Web services to achieve interchangeability between heterogeneous resources. In addition, to enhance the efficient use of learning resources by students, the study proposes a hybrid algorithm based on collaborative filtering algorithm and sequential pattern mining algorithm to achieve personalized sequential recommendation for students. The results show that the platform created by the study exhibits excellent performance in terms of resource transfer capability, achieves efficient teaching resource sharing in a short response time and also shows that the proposed recommendation algorithm is highly accurate.

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners’ learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners’ current skill level and learning goals outperforms the other approaches in practice problem recommender systems.

CIP- Efficient Method for Mining Frequent Itemsets from Data streams using Landmark Window Model

Continuous stream transactions like network monitoring, retail market data analysis and stock market prediction need the “frequent patterns” to be detected recurrently. Literature suggests that several pattern mining solutions are being developed over years. Still lot of challenges need to be addressed due to rapidness in generation of continuous, unbounded and ordered data real time. Hence extraction of frequent patterns from recent data will improve the analysis of stream data. In this article, a new landmark window model CIP (candidate indexing and pruning) is considered for mining the datasets. CIP allows us to mine over entire history of data streams, which improves the accuracy. This article also proposes the candidate indexed sub (CIS)-tree scheme to extract the essential information from each incoming transactions of data streams. Our proposal is compared with the existing “improved data stream mining” (ISDM) for maximal frequent itemsets algorithm. Extensive experimental analyses prove the superiority of the proposed CIP over the popular ISDM in terms of accuracy and time complexity for high-speed data stream. This article also covers up a case study where the proposed approach is applied for an application called “web prefetching”.