Frequent Pattern Mining Research Articles

Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data

ABSTRACT Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

Beyond Frequency

Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum utility threshold ( minutil ) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the “ rare item problem ” and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.

Secure and unifold mining model for pattern discovery from streaming data

The intimidating challenge is practice of data mining (DM) over the streams of data because of its continuous data streaming. On the data streams, the practices of mining should be performed on cluster of streamed records in specified interval of time. The representation of window is the buffered records set which might be dynamic or static in the size. When compared with other practices of mining, the 'frequent pattern mining' on the streams of data are crucial. This occurs because, for predicting the pattern frequency, many of the existing methods repeatedly scan entire buffered transactions. This denotes the intricacy of procedure and overhead of memory. This paper proposes novel DM algorithms in particular for identifying the frequent patterns from indefinite data streams which scans every window once, therefore windows buffered records is pruned that evades computational and memory overhead. 'Unifold mining model for pattern discovery from streaming data' is the contribution of this paper. The outperformance of UMM when compared with other contemporary models is represented by crucial assessment of algorithm and optimisation schemes.

Secure and unifold mining model for pattern discovery from streaming data

The intimidating challenge is practice of data mining (DM) over the streams of data because of its continuous data streaming. On the data streams, the practices of mining should be performed on cluster of streamed records in specified interval of time. The representation of window is the buffered records set which might be dynamic or static in the size. When compared with other practices of mining, the 'frequent pattern mining' on the streams of data are crucial. This occurs because, for predicting the pattern frequency, many of the existing methods repeatedly scan entire buffered transactions. This denotes the intricacy of procedure and overhead of memory. This paper proposes novel DM algorithms in particular for identifying the frequent patterns from indefinite data streams which scans every window once, therefore windows buffered records is pruned that evades computational and memory overhead. 'Unifold mining model for pattern discovery from streaming data' is the contribution of this paper. The outperformance of UMM when compared with other contemporary models is represented by crucial assessment of algorithm and optimisation schemes.

A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

The mining of frequent weighted patterns (FWPs) that considers the different semantic significance (weight) of items is more suitable for practice than the mining of frequent patterns. Therefore, it plays a vital role in real-world scenarios. However, there exist several limitations when applying methods for mining FWPs designed for static data on growth datasets, especially data streams. Hence, this study proposes an algorithm for mining FWPs over data streams. First, we introduce the concept of mining FWPs over data streams via a sliding window model. Then, we introduce a modification of the weighted node tree (WN-tree) named SWN-tree that has the ability to maintain the information over data streams. Next, this study develops a method for mining FWPs over data streams employing a sliding window model based on SWN-tree. This method is called FWPODS (Frequent Weighted Patterns Over Data Stream) algorithm. Finally, we conduct empirical experiments to compare the performances of our approach and the state-of-the-art algorithm (NFWI) for mining FWPs over data streams. The results of experiment indicate that our approach outperforms the NFWI algorithm when running in batch mode in a sliding window.

A Vibrational Signal Fault Diagnosis Rule Extraction Method Based on DST‐ACI Discriminant Criterion

A fault diagnosis rule extraction method oriented to machine foot signal based on dynamic support threshold and association coefficient interestingness (DST‐ACI) discriminant criterion is proposed in this paper. The new method includes three main innovations. First, the feature state coding method based onK‐means clustering fully takes into account the imbalanced distribution of signal feature values due to the noise interference, and divide the signal feature values into several range intervals to generate the feature state code. Second, the frequent feature pattern mining method based on dynamic support threshold (DST) discriminant criterion can dynamically adjust support threshold according to the frequency of the feature states in each candidate pattern. Third, the fault diagnosis rule extraction method based on the association coefficient interestingness (ACI) discriminant criterion introduces a new metrics called ACI to evaluate the correlation between the pattern and the fault. Four types of fault simulation experiments were carried out, and the performance of the DST‐ACI method was tested using the collected vibration signal. The results show that compared with the coding method based on equal‐width discretization or equal‐density discretization, the accuracy of the transactional dataset generated by the feature state coding method based on K‐means clustering is higher. Compared with the frequent feature pattern mining method based on the constant support threshold criterion, the pattern mined by the DST‐based criterion has generally higher support. Compared with the existing confidence‐lift‐based and confidence‐improint‐based fault diagnosis rule extraction frameworks, the positive correlation between the feature states and the fault type of the rules extracted based on the DST‐ACI framework is generally stronger.

Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining

Many contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders' behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns analysis. However, there are only a few studies that have explored the stakeholders' behaviour over a more extended period of several academic years in detail. This article contributes to filling in this gap and provides a novel approach to using homogeneous groups of frequent patterns for identifying the changes in stakeholders' behaviour from the perspective of time. The novelty of this approach lies in fact, that even though the time variable is not directly involved, identification of homogeneous groups of frequent itemsets allows analysis and comparison of the stakeholders' behavioral patterns and their changes over different observed periods. Found homogeneous groups of frequent itemsets, which conform minimal threshold of selected measures, showed, that it is possible to uncover the changes in stakeholders' behaviour throughout the observed longer period. As a result, these homogenous groups of found frequent patterns allow a better understanding of the hidden changes in seasonality or trends in stakeholders' behaviour over several academic years. This article discusses the possible implications of the results and proposed approach in the context of virtual learning environment management and educational content improvement.

A Distributed Method for Fast Mining Frequent Patterns From Big Data

In recent years, knowledge discovery in databases provides a powerful capability to discover meaningful and useful information. For numerous real-life applications, frequent pattern mining and association rule mining have been extensively studied. In traditional mining algorithms, data are centralized and memory-resident. As a result of the large amount of data, bandwidth limitation, and energy limitations when applying these methods to distributed databases, especially in this era of big data, the performance is not effective enough. Hence, data mining on distributed environments has emerged as an important research area. To improve the performance, we propose a set of algorithms based on FP growth that discover FPs that are capable of providing fast and scalable service in distributed computing environments and a brief data structure to store items and counts to minimize the data for transmission on the network. To ensure completeness and execution capability, DistEclat and BigFIM were considered for the experiment comparison. Experiments show that the proposed method has superior cost-effectiveness for processing massive datasets and good capabilities under various experiment conditions. The proposed method on average required only 33% of the execution time and 45% of the transmission cost of DistEclat. Compared to BigFIM, The proposed method on average required 23.3% of the execution time and 14.2% of the transmission cost of BigFIM.

An Approach for Seasonally Periodic Frequent Pattern Mining in Retail Supermarket

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.

Empirical Review of Trends in the Implementation of Frequent Pattern Mining for E-Commerce

Customers commonly need to gather even more details about an item prior to purchasing. They usually reflect on the view of additional customers to help to make a decision on their buy. Today, various websites possess been lately created that emphasis the involvement of end users. Several of the websites many of these as Amazon.com prospects persons to create their judgment about the items and talk about regarding the features of that merchandise. It gives a grasp info sources on the internet. Acquiring all such reviews assists manufacturers to be conscious of the weakness and strengths of their solution to increase it.

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε‐differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type‐2 Fuzzy Sets over Medical Data Stream

Real‐time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type‐2 fuzzy set theory over data stream (WSWFFP‐T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type‐2 fuzzy set theory (WFFPT2‐tree) and fuzzy‐list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP‐T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.

What Are the Characteristics of the Ones That Behave Better to Control the Pandemic?

Background: Human behavior is an important determinant of health outcomes. In addition to intrinsic public health challenges associated with the COVID-19 pandemic, in many countries, some individuals decided not to be vaccinated, streets were full of people, and businesses struggling to recover were partially open despite lockdown or "stay at home instructions". These behaviors contrast with the clear benefit of distancing, use of masks, and vaccination to decrease collective and individual risks. Here, we try to understand what are the social and working characteristics in Brazil related to appropriate protective behavior reports. Which conditions are associated with it being easier or more difficult to comply with protective measures?Methods: We selected three common selfcare procedures: mask use, distancing by at least 1 meter when out, washing hands or use of alcohol, combined with assessing the social context of people who reported to comply with all three measures. Using a frequent pattern mining perspective, association rules were generated from a set of answers to questions that co-occur with at list a given frequency identifying the pattern of characteristics of the groups divided according to the protective behavior report.Findings: We identify patterns composed by social and working determinants of the compliance of protective self-care measures.Interpretation: The rationale was to identify a pool of working and social characteristics that might have better adhesion to behaviors and self-care measures. This search might be helpful to identify constraints to fully follow public policies.Funding Information: The present study was partially funded by the Pan American Health Organization (PAHO/WHO) in Brazil.Declaration of Interests: All authors declare no financial or personal relationships with other people or organisations in potential conflict of interest, even if it does not directly relate to the submitted work.Ethics Approval Statement: This online study was approved by the National Commission of Ethics in Research (CONEP) on May 2nd, 2020 (CAAE #: 30823620.6.0000.5149) and complies with the Helsinki Declaration (1989). All participants were informed that the survey would take about 25 minutes to be completed. The consent form was presented in the first page of the online form and only participants who consented to their participation were further enrolled.

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals.

Traditionally, mental health specialists monitor their patients’ social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson’s correlation coefficient >70%) with individuals’ social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

Mining frequent patterns (FP) is an essential task in data mining. The parameter required for this task is typically the minimum support threshold. Tuning this parameter to a suitable value is a difficult task, especially for inexperienced users. Thus, the Top-Rank-K frequent patterns mining problem was introduced. It requires the user to input an easily-evaluated parameter, K, in order to obtain the set of all frequent patterns from the most frequent to the Kth rank of frequency. In this paper, we customize three general Frequent Pattern Mining (FPM) algorithms, namely FIN, PrePost, and PrePost+, to develop specialized Top-Rank-K FP mining algorithms: TK_FIN, TK_PrePost, and TK_PrePost+. The Dynamic Minimum Support Raising strategy is applied on these algorithms to ensure efficiency. Experimentally, we evaluate the performance of these algorithms against an original, efficient, Top-Rank-K algorithm, BTK. The three presented algorithms perform 90% better than BTK in most of the experiments, with respect to runtime. Between the three Top-Rank-K FPM algorithms we present, TK_FIN achieves the best performance from both runtime and memory consumption perspectives.

Mining frequent approximate patterns in large networks

AbstractFrequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.

Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop

Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop

Observational Studies on Algorithms of Frequent Pattern Mining

Data mining is an extensive research area, as frequent pattern mining performs an effective role in many real-life applications. Frequent patterns are used in data mining with multiple algorithms that give different performances over different datasets. Often, the initial basic algorithms for pattern mining like Apriori, FP Growth, and Eclat are used. The cornerstone of this manifesto is to explore the major issues/challenges related to the algorithms used with the transaction Subdata of continuous mining of pattern.

VertTIRP: Robust and efficient vertical frequent time interval-related pattern mining

Time-interval-related pattern (TIRP) mining algorithms find patterns such as “A starts B” or “A overlaps B”. The discovery of TIRPs is computationally highly demanding. In this work, we introduce a new efficient algorithm for mining TIRPs, called vertTIRP which combines an efficient representation of these patterns, using their temporal transitivity properties to manage them, with a pairing strategy that sorts the temporal relations to be tested, in order to speed up the mining process. Moreover, this work presents a robust definition of the temporal relations that eliminates the ambiguities with other relations when taking into account the uncertainty in the start and end time of the events (epsilon-based approach), and includes two constraints that enable the user to better express the types of TIRPs to be learnt. An experimental evaluation of the method was performed with both synthetic and real datasets, and the results show that vertTIRP requires significantly less computation time than other state-of-the-art algorithms, and is an effective approach.