Pattern Mining Approach Research Articles

An efficient approach for incremental erasable utility pattern mining from non-binary data

An efficient approach for incremental erasable utility pattern mining from non-binary data

A Secure Parallel Pattern Mining System for Medical Internet of Things.

In this paper, a new generic parallel pattern mining framework called multi-objective Decomposition for Parallel Pattern-Mining (MD-PPM) is developed to solve challenges in the Internet of Medical Things through big data exploration. MD-PPM discovers important patterns by using decomposition and parallel mining methods to explore connectivity between medical data. First, a new technique, the multi-objective k-means algorithm, is used to aggregate medical data. A parallel pattern mining approach based on GPU and MapReduce architectures is also used to create useful patterns. To ensure complete privacy and security of the medical data, blockchain technology has been integrated throughout the system. Several tests were conducted to demonstrate the high performance of two sequential and graph pattern mining problems on large medical data and to evaluate the developed MD-PPM framework. From our results, our proposed MD-PPM has achieved strong results in terms of memory usage and computation time in terms of efficiency. Moreover, MD-PPM performs well in terms of accuracy and feasibility compared to existing models.

Practice challenge recommendations in online judge using implicit rating extraction and utility sequence patterns

PurposeThe existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.Design/methodology/approachTo achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.FindingsExperiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.Originality/valueThe proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

A Novel and Efficient Spatio-Temporal Co-location Pattern Mining Algorithm

Colocation pattern mining approaches aim at discovering neighboring relationships of distinct spatial features in geographic and temporal space.With big spatio-temporal dataset, there is large number of patterns often discovered. Then it is of importance to discover meaningful and patterns of interest which come as an aid in applications in use for humans and commercial use. To discover interesting patterns, we present in this article a co-location pattern mining algorithmic framework by considering neighbourhood, clustering, hashing, and mining methods. Neighbourhood relationship describes the closeness between the entities. The results of neighbourhood could be varied by varying the distance threshold. These objects exhibiting neighbouring entities are grouped using clustering technique. Clustering is a classical research approach that produces grouping of the entities. A clustering technique has been presented in the paper for spatio-temporal dimension that offers the advantage of faster grouping based on the neighbourhood relationship. Finally, a hash structure is utilized for faster access and retrieval of patterns. The proposed mining algorithm along with the distance and time threshold efficiently discovers the interesting spatio-temporal patterns and validates the patterns. The experiment conducted shows that the proposed co-location algorithm method yields effective and efficient outcome.

A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. High Utility Co-location Pattern Mining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude.

Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectories

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

Modelling Player Skills in Rocket League through a Behavioural Pattern Mining Approach

Modelling Player Skills in Rocket League through a Behavioural Pattern Mining Approach

Optimizing School Management through Genetic Algorithm-Driven Pattern Mining for Educational Process Enhancement

Improving school administration procedures is a crucial goal in the constantly changing field of education. Genetic algorithms (GA), influenced by natural selection, repeatedly examine possible solutions, assess their suitability, and progress towards the best patterns. In educational settings, this repeated improvement process enables the identification of concealed patterns in various datasets, such as student performance statistics, resource distribution trends, and administrative procedures. By using genetic algorithms, policymakers and educators have access to an advanced set of tools for making decisions based on data and improving processes. This paper introduces an Educational Optimization Genetic Algorithm (EOGA), a new approach for pattern mining to enhance school management operations. The suggested method deals with the changing nature of educational leadership by providing flexibility to adapt to different situations and developing priorities. Genetic algorithms help continuously improve and optimize school administration procedures, allowing institutions to adjust to new challenges and possibilities. By using genetic algorithm-based pattern mining, educators and others can discover detailed insights that standard analytical methods could miss. These understandings help administrators make educated decisions about resource distribution, curriculum improvement, and student assistance, leading to better educational results and organizational efficiency. By adopting a data-focused method, schools can go beyond conventional thinking and make ongoing enhancements in several aspects of school administration. Incorporating genetic algorithms into educational practices significantly promotes creativity, effectiveness, and high quality in education.

An irregular CLA-based novel frequent pattern mining approach

An irregular CLA-based novel frequent pattern mining approach

An Irregular CLA-based Novel Frequent Pattern Mining Approach

An Irregular CLA-based Novel Frequent Pattern Mining Approach

A Data-Driven Three-Stage Adaptive Pattern Mining Approach for Multi-Energy Loads

A Data-Driven Three-Stage Adaptive Pattern Mining Approach for Multi-Energy Loads

Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining.

Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.

Tensor-Train-Based Multiuser Multivariate Multiorder Physical Markov Process Informed Multimodal Prediction for Industrial Trajectory Applications

By combining data driven approaches and physical laws for industrial trajectory prediction can improve the performance of industrial applications, such as path planning of robots, routing selection of vehicles in transportation system. In the era of industrial big data, the application of industrial trajectory prediction based on physical Markov process and tensor model has attracted much attention. To synchronously improve the prediction accuracy and computational efficiency, this paper proposes a tensor-train (TT) based multiuser multivariate multiorder (3M) physical Markov prediction approach for multimodal industrial trajectory pattern mining. First, we propose a TT-based unified product calculation rule with its scalable computation approach based on decomposed TT cores to speed up the execution efficiency. Then, a TT-based 3M (TT-3M) Markov transition approach is presented. Furthermore, we put forward a TT-based power method to calculate the stationary joint eigentensor (SJE) and an SJE-based multimodal prediction algorithm to mine concealed trajectory patterns. Experimental results based on real GPS trajectory dataset show that compared with the original tensor-based 3M approach, the TT-3M approach can improve the computational efficiency up to 3 times and reduce the memory space proportion to a minimum of 1‰ while ensuring basically consistent prediction accuracy.

An advanced approach for incremental flexible periodic pattern mining on time-series data

Periodic pattern mining is a topic for mining periodic event patterns with sufficient confidence. The resulted patterns are often used to predict future events because they have established confidence that their periodicity will be maintained after the last time of the given data. Therefore, intelligent decision-making through periodic patterns has led to popularity in various applications, such as oil price fluctuations prediction, traffic congestion prediction, human behavior analysis, and sensor-based data analysis in the industry. Meanwhile, previous approaches adopting data structures based on the suffix-tree or trie developed the mining performance of this topic, and a concept that flexible periodic patterns consider don’t-care events in their intermediate events increased the flexibility of the results in this field. However, the data structures have limitations in terms of computing performance. Meanwhile, technological development has made collecting and accumulating data faster and faster. To conduct mining on modern databases which keep increasing incrementally, processing data streams in real time should be considered in the mining method. In this paper, we propose a novel flexible periodic pattern mining approach that can operate on incremental time-series databases by utilizing an enhanced data structure. Moreover, various experiments, such as multivariable analysis, sensitivity analysis, scalability analysis, demonstrate that the proposed approach has superior performance over previously proposed state-of-the-art algorithms in terms of runtime and memory usage.

Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis

Sepsis is one of the most challenging health conditions worldwide, with relatively high incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially irreversible organ dysfunction. However, data-driven early identification of sepsis is challenging as sepsis shares signs and symptoms with other health conditions. This paper adopts a temporal pattern mining approach to identify frequent temporal and evolving patterns of physiological and biological biomarkers in sepsis patients. We show that using these frequent patterns as features for classifying sepsis and non-sepsis patients can improve the prediction accuracy and performance up to 7%. Most of the temporal modeling approaches adopted in the sepsis literature are based on deep learning methods. Although these approaches produce high accuracy, they generally have limited model explainability and interpretability. Using the adopted methods in this study, we could identify the most important features contributing to the patients’ sepsis incidence, such as fluctuations in platelet, lactate, and creatinine, or evolution of patterns including renal and metabolic organ systems, and consequently, enhance the findings’ clinical interpretability.

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.

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.

A constraint programming approach for quantitative frequent pattern mining

A constraint programming approach for quantitative frequent pattern mining

A constraint programming approach for quantitative frequent pattern mining

A constraint programming approach for quantitative frequent pattern mining

High Utility Pattern Mining: A Survey on Current and Possible Areas of Applications

High Utility Pattern Mining (HUPM) has a wide range of applications, including making recommendations, detecting outliers, analyzing customer behaviors, and solving a wide range of other problems. In fact, unlike other important data mining tasks such as outlier analysis and classification, high utility pattern mining can be used as an intermediary tool for providing pattern-centered insights for other data mining tasks. In this paper, we look at a wide range of different applications of high utility pattern mining that are available in the literature. We gathered the literature review papers from different resources based on the keywords related to the applications of high utility pattern mining. Then we classified the existing research papers into different categories according to the domain application, then we summarize the proposed method of each paper and the datasets used for the evaluation process. Rather than providing a full discussion of every possible application, the objective of this paper is to give readers an overview of what is feasible when high utility pattern mining approaches are used. This paper can help researchers to apply the techniques of high utility pattern mining to solve real-time problems and it can help to explore other possible domains to apply high utility pattern mining techniques by proposing new methods and evaluating them accordingly.