Frequent Sequential Pattern Mining Research Articles

Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event Sequences

Frequent sequential pattern mining is a valuable technique for capturing the relative arrangement of learning events, but current algorithms often return excessive learning event patterns, many of which may be noise or redundant. These issues exacerbate researchers’ burden when interpreting the patterns to derive actionable insights into learning processes. This study proposed permutation tests for identifying sequential patterns whose occurrences are statistically significantly greater than the chance value and different from their superpatterns. Simulations demonstrated that the test for detecting sound patterns had a low false discovery rate and high power, while the test for detecting nonredundant patterns also showed a high accuracy. Empirical data analyses found that the patterns detected in training data were generalizable to test data.

Network-based visualisation of frequent sequences.

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.

Classification Based Neural Network Modelling with Continuous and Sequential data

Data mining is a potential process that involves finding useful data from an existing database. The main core work of the paper is classification with Neural Networks and Pattern mining. The study is involved in figuring out the efficient and accurate algorithm from the existing mining algorithms. The sequential mining algorithms are incorporated in areas where the data are in the form of transactions. However, in this paper, the closed and frequent sequential mining algorithms are looked at deeper with the neural network Based Multi-Layer Perceptron to pick out a productive algorithm. There are a lot of potential sequential pattern mining algorithms that can be used on different types of data. And some are good with results when they are worked with certain datasets. The frequent sequential algorithms produce the data based on the ‘minsup’ value provided. Unlike the other mining algorithms, the closed pattern mining algorithms are compact and lossless when producing results. In the frequent sequential pattern mining category, the lattice-based CM-SPADE algorithm stands out with high accuracy in terms of memory management in megabytes and execution time in milliseconds. The CM-ClaSP algorithm performs better than the ClaSP, CloFAST and BIDE+ algorithms in the closed sequential pattern mining algorithm. Now the performance of the CM-SPADE has been added with the classification based format Multi-Layer Perceptron to boost up the Perceptron Networks Classification Style with an outcome of 93% of accuracy.

Detecting malicious transactions in database using hybrid metaheuristic clustering and frequent sequential pattern mining

Detecting malicious transactions in database using hybrid metaheuristic clustering and frequent sequential pattern mining

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

Extracting Frequent Sequential Patterns of Forest Landscape Dynamics in Fenhe River Basin, Northern China, from Landsat Time Series to Evaluate Landscape Stability

The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel method for analyzing the dynamics of landscape patterns. First, patch density (PD), largest patch index (LPI), landscape shape index (LSI), and contiguity index (CI) were used to identify the types of forest spatial patterns. The frequent sequential pattern mining method was used to detect the frequent subsequences from the time series of landscape pattern types from 1991 to 2020 and further evaluate the forest landscape stability of the Fenhe River Basin in China. The results show that different frequent sequence patterns have conspicuous spatial and temporal differences, which describe the evolution processes and stability changes during a certain period of forest evolution and play an important role in the analysis of forest dynamics. The proportion of the disturbed regions to the total forest area exhibited a downward trend. The long-term evolution pattern indicates that there are many evolution processes and trends in the forest at the same time, showing an aggregation distribution law. Compared with 2016, the forest landscape has become complete in 2020, and the overall stability of the Fenhe River Basin has improved. This study can provide scientific support to land managers and policy implementers and offer a new perspective for studying forest landscape pattern changes and evaluating landscape stability.

Weighted frequent sequential pattern mining

Trillions of bytes of data are generated every day in different forms, and extracting useful information from that massive amount of data is the study of data mining. Sequential pattern mining is a major branch of data mining that deals with mining frequent sequential patterns from sequence databases. Due to items having different importance in real-life scenarios, they cannot be treated uniformly. With today’s datasets, the use of weights in sequential pattern mining is much more feasible. In most cases, as in real-life datasets, pushing weights will give a better understanding of the dataset, as it will also measure the importance of an item inside a pattern rather than treating all the items equally. Many techniques have been introduced to mine weighted sequential patterns, but typically these algorithms generate a massive number of candidate patterns and take a long time to execute. This work aims to introduce a new pruning technique and a complete framework that takes much less time and generates a small number of candidate sequences without compromising with completeness. Performance evaluation on real-life datasets shows that our proposed approach can mine weighted patterns substantially faster than other existing approaches.

“Musicalization of the Culture”: Is Music Becoming Louder, More Repetitive, Monotonous and Simpler?

“Musicalization of the culture” is the social science concept proposed by American philosopher George Stainer. He depicted the glooming future of music—it would become omnipresent while having increasing volume, repetitiveness, and monotony, which are ascribed to the debase of literal aesthetics. Although research that relates to one or some of these predictions exists, neither of them encompass all these “musicalization” manifestations, nor do they study the trend of these predictions over time. Therefore, this preliminary research tries to validate whether music has gained acoustic loudness, and lyrical repetitiveness, monotony, and simplicity in a computational fashion. Conducting time-series analysis with trend detection, we confirmed the increasing trends of acoustic loudness and repetitiveness but not monotony and simplicity from 1970 to 2016 using the MetroLyrics dataset and Spotify API. To investigate the simultaneity of these trends, we further conducted synchrony analysis and found little evidence indicating they would influence each other in a lagged fashion. In light of the results, we briefly discussed our findings by relating to the music industry change in reality. Our research made the first attempt to answer this music sociological preposition. On top of this, we also proposed novel metrics to quantify repetitiveness using closed frequent sequential pattern mining, which could be illuminating for future research.

“Musicalization of the Culture”: Is Music Becoming Louder, More Repetitive, Monotonous and Simpler?

“Musicalization of the culture” is the social science concept proposed by American philosopher George Stainer. He depicted the glooming future of music—it would become omnipresent while having increasing volume, repetitiveness, and monotony, which are ascribed to the debase of literal aesthetics. Although research that relates to one or some of these predictions exists, neither of them encompass all these “musicalization” manifestations, nor do they study the trend of these predictions over time. Therefore, this preliminary research tries to validate whether music has gained acoustic loudness, and lyrical repetitiveness, monotony, and simplicity in a computational fashion. Conducting time-series analysis with trend detection, we confirmed the increasing trends of acoustic loudness and repetitiveness but not monotony and simplicity from 1970 to 2016 using the MetroLyrics dataset and Spotify API. To investigate the simultaneity of these trends, we further conducted synchrony analysis and found little evidence indicating they would influence each other in a lagged fashion. In light of the results, we briefly discussed our findings by relating to the music industry change in reality. Our research made the first attempt to answer this music sociological preposition. On top of this, we also proposed novel metrics to quantify repetitiveness using closed frequent sequential pattern mining, which could be illuminating for future research.

Recognizing patterns of student\u2019s modeling behaviour patterns via process mining

Evaluating student learning effect plays an essential role in education, which is typically done by assessing student’s final deliverables. However, the student’s learning process has not been properly explored in the past.In this paper, we propose an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining. A comprehensive online modeling platform has been developed to enable data collection. We have carried out a case study, in which we applied our approach to a real teaching scenario, consisting of student online modeling behavior data collected from 24 students majoring in computer science. We also associate our process mining results with the numeric evaluation values. The preliminary result of case analysis has shown good potential to mine student modeling patterns and interpret their behaviors, contributing to student learning effect evaluation.

Frequent Sequential Patterns (FSP) Algorithm for Finding Mutations in BRCA2 Gene

The Sequential Pattern Mining (SPM) is a fundamental task in data mining. The SPM mines subsequences from given sequence which can be used for various analyses. This paper aims to propose an efficient method for mining frequent sequential patterns in biological data. It also includes the k-mer for decomposing the sequence according to the user defined threshold value. The input data used is breast cancer gene BRCA2 normal and mutated BRCA2 gene. The parameters used for analyses are suffix, candidate pattern and frequent pattern. The suffix value is increased for mono-,di and tri-nucleotide in mutated gene and in frequent pattern tri-nucleotide has increased nucleotide in mutated gene. So this abnormal increase in pattern may leads to cancer in the human

Frequent Sequential Pattern Mining in Web Log Data A Simple Approach

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns

Traditional frequent sequential pattern mining only considers the time point-based item or event in the patterns. However, in many application, the events may span over multiple time points and the relations among events are also important. Time interval-based pattern mining is proposed to mine the interesting patterns of events that span over some time periods and also by considering the relations among events. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time duration may cause different results. In this work, we propose two algorithms, SARA and SARS, for mining frequent time interval-based events with duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. For the performance evaluation, we propose a naive algorithm and modify a previous algorithm along with the implementation of SARA and SARS. The experimental results show that SARA and SARS are more efficient in terms of execution time and memory usage than other two algorithms. Moreover, we extend this work by considering utility value or importance of event in each time stamp. Therefore, we propose another new High Utility Time Interval-based Events with Duration, HU-TIED, pattern. HU-TIED incorporates the concept of utility pattern mining and TIED pattern mining. We design an algorithm, LMSpan, to mine top-K HU-TIED patterns. For the performance evaluation, we design a baseline algorithm, GenerateNCheck to compare with LMSpan. LMSpan takes less time and memory and generates less candidates than GenerateNCheck.

MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE

Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process

Similarity analysis of frequent sequential activity pattern mining

Activity pattern classification is extensively studied using multi-person single-day mobile traces. However, human mobility exhibits intra-personal variability and thus single-day activity may not fully capture one’s activity patterns. This paper creates a methodological framework to analyze similarity of activity patterns using frequent sequential pattern mining when multi-person multi-day data is available. Frequent sequential pattern mining discovers the frequently occurring ordered subsequences and is a natural approach of analyzing multi-day travel patterns. Prefix-Span algorithm is implemented to extract frequent patterns for each individual. Then similarity measures are defined to describe the extent to which two travelers’ activity patterns are alike and the regularity of how one repeats her activity patterns from day to day. Based upon the pairwise similarity values between two individuals, hierarchical clustering is conducted to divide travelers into communities. To illustrate these methodologies, 349 travelers’ 19,130 travel activity sequences are extracted from the world’s first connected vehicle testbed in Ann Arbor, Michigan. Three major clusters are identified. Coupled with demographics, these clusters are characterized as “seniors”, “working class”, and “parents”, respectively. Multinomial logistic regression is employed to model to what extent the similarity of socio-demographics can explain that of travel patterns. This work can be extended to either infer an unknown user’s demographics (or customer profiling) based on her activity patterns, or to reconstruct an unknown user’s frequent activity patterns based on her demographics and other similar travelers’ patterns.

Predicting Long-Term Trajectories of Connected Vehicles via the Prefix-Projection Technique

The vehicle location prediction based on their spatial and temporal information is an important and difficult task in many applications. In the last few years, devices, such as connected vehicles, smart phones, GPS navigation systems, and smart home appliances, have amassed the large stores of geographic data. The task of leveraging this data by employing moving objects database techniques to predict spatio-temporal locations in an accurate and efficient fashion, comprising a complete trajectory remains an actively researched area. Existing methods for frequent sequential pattern mining tend to be limited to predicting short-term partial trajectories, at extremely high computational costs. In order to address these limitations, we designed a prefix-projection-based trajectory prediction algorithm called PrefixTP, which contains three essential phases. First, data collection, connected vehicles equipped with sensors comprise a vehicle grid and generate copious amounts of spatio-temporal data, in order to communicate and share traffic information. Second, model training, examining only the prefix subsequences, and projecting only their corresponding postfix subsequences into projected sets. Finally, trajectory matching, recursively finding postfix sequences meeting the requirement of minimum support count, and outputting the most frequent sequential pattern as the most probable trajectory. Fundamentally, PrefixTP supports three trajectory matching strategies which encompass all possibilities of prediction. Extensive experiments were conducted using real world GPS data sets, and the results show, when comparing predicted complete trajectories against partial short-term trajectories with a guarantee of real-time forecasting, that PrefixTP outperforms first-order, second-order Markov models, and Apriori-based trajectory prediction algorithm.

Mining top- k co-occurrence items with sequential pattern

The problem of mining top-k co-occurrence items with sequential pattern is defined.Sid-set and Sid-sets with an index are used to reduce the scanning time.Three algorithms including NAM, VAM, and VIAM for mining top-k co-occurrence items with sequential pattern are proposed.Two pruning techniques are proposed and used in VAM and VIAM algorithms to improve the processing time. Frequent sequential pattern mining has become one of the most important tasks in data mining. It has many applications, such as sequential analysis, classification, and prediction. How to generate candidates and how to control the combinatorically explosive number of intermediate subsequences are the most difficult problems. Intelligent systems such as recommender systems, expert systems, and business intelligence systems use only a few patterns, namely those that satisfy a number of defined conditions. Challenges include the mining of top-k patterns, top-rank-k patterns, closed patterns, and maximal patterns. In many cases, end users need to find itemsets that occur with a sequential pattern. Therefore, this paper proposes approaches for mining top-k co-occurrence items usually found with a sequential pattern. The Naive Approach Mining (NAM) algorithm discovers top-k co-occurrence items by directly scanning the sequence database to determine the frequency of items. The Vertical Approach Mining (VAM) algorithm is based on vertical database scanning. The Vertical with Index Approach Mining (VIAM) algorithm is based on a vertical database with index scanning. VAM and VIAM use pruning strategies to reduce the search space, thus improving performance. VAM and VIAM are especially effective in mining the co-occurrence items of a long input pattern. The three algorithms were evaluated using real-world databases. The experimental results show that these algorithms perform well, especially VAM and VIAM.

Grade-based Spatio-Temporal Sequential Pattern Mining using Support and Event Index Measures

Objectives: The knowledge on cause-effect relationships between instances of real-world entities can be gathered by extracting sequential patterns from spatio-temporal databases. The discovery of the patterns in the context of space and time is a challenging issue. The sequential pattern mining algorithms designed for traditional databases may result in the loss of spatio-temporal correlations due to the improper estimations of properties related to the time and space. The proposed work approaches the problem of designing sequential pattern mining algorithm specifically for spatio-temporal event datasets. Methods/Statistical Analysis: An algorithm is proposed which is based on frequency-based measures for mining frequent spatio-temporal sequential patterns. The spatio-temporal sequential pattern mining based on Support index and Event index algorithm proposes two new parameters support index and event index which are used to scrutinize the sequences extracted from the database. A data structure is also proposed to represent the spatio-temporal data for efficient pattern mining. Findings: The proposed algorithm generates the interesting set of frequent sequential patterns. The proposed algorithm is compared with Slicing-STS-Miner and MST-ITP and the experimental results proved that the proposed algorithm performs well with the order of two to three. Application/Improvements: The proposed algorithm uses frequency-based measures rather than density-based measures. Frequency-based measures take less computational time when compared to density-based measures. The proposed technique is suitable for extracting knowledge in the form of sequential patterns from spatio-temporal point databases.

An Improved Algorithm Research on the PrefixSpan Based on the Server Session Constraint

When we mine long sequential pattern and discover knowledge by the PrefixSpan algorithm in Web Usage Mining (WUM).The elements and the suffix sequences are much more may cause the problem of the calculation, such as the space explosion. To further solve the problem a more effective way is that. Firstly, a server session-based server log file format is proposed. Then the improved algorithm on the PrefixSpan based on server session constraint is discussed for mining frequent Sequential patterns on the website. Finally, the validity and superiority of the method are presented by the experiment in the paper.

Discovery of deep order-preserving submatrix in DNA microarray data based on sequential pattern mining

In recent years, order-preserving submatrix (OPSM) model has been widely used in gene expression data analysis. Since it focuses on the changes between the elements rather than the real value, it shows better robustness and statistical significance among results than other models do. Currently, many OPSM algorithms are heuristic. They cannot mine all OPSMs as well as the deep OPSMs which are of biological significance in gene expression data. In this paper, an exact algorithm is proposed to find OPSMs by using frequent sequential pattern mining method. Firstly, we find out all common subsequences (ACS) between any two rows through dynamic programming. Then, we store them into a suffix tree. After that, we can get all OPSMs in this suffix tree, including deep OPSMs. Verified by the real gene data and artificially synthesised data, it is proved that our algorithm is efficient and meaningful.