Temporal Association Rule Mining Research Articles

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests.

GAN-based Temporal Association Rule Mining on Multivariate Time Series Data

GAN-based Temporal Association Rule Mining on Multivariate Time Series Data

Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage

Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages.

Research on Fuzzy Temporal Event Association Mining Model and Algorithm

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.

Web Personalized Recommendation Model Using Temporal Fuzzy Association Rule Mining

Abstract: A web access log file contains timely sequenced log entries which include essential fields to indicate user activities. Analysis of these patterns provides valuable information for web designer to quickly respond to their individual needs. Many industries are struggling to retain regular interested customers for the improvement of customer relationship. Retrieval of relevant information automatically from these log files for interested group of users is a difficult process, since acquiring interested user profiles which evolves continuously with respect to time are not so easy. The paper presents a novel Web Personalized Recommendation Model (WPRM) using temporal fuzzy association rule mining technique. Temporal Fuzzy Association Rule Mining (FTARM) technique is proposed and applied on a focused set of interested users to provide intelligent recommendations. The proposed model results in less execution time and reduced memory utilization with high accuracy.

An Efficient Temporal Inter-Object Association Rule Mining Algorithm on Time Series

Time series is acknowledged as one of the most common crucial data types in our daily lives. Among the time series mining tasks, rule discovery is important to provide valuable knowledge that brings us a profound insight view of relationships between different objects through time. One challenge is that when the number of objects and their lengths increase, it easily leads to a combinatorial explosion. Therefore, we propose a temporal inter-object association rule mining algorithm, NPTR, to discover new informative temporal inter-object association rules from time series and overcome the challenge with parallelization. Another remarkable point is that NPTR defines a concurrent approach by performing the frequent pattern mining process and rule mining one simultaneously. From the experiments on real-world data, NPTR returns the rules exactly with less time and memory costs than others do. Those rules can be further utilized for other tasks such as prediction, classification, and clustering.

Incremental sequential patterns for multivariate temporal association rules mining

Most temporal association rule mining algorithms can mine the temporal relationship between items, but the sequential relations between successive items remain unknown, while sequential pattern mining algorithms can discover the sequential relationship between successive items but the quantitative time interval of sequential patterns remains unknown. Furthermore, they only focus on mining a historical temporal database, which ignores the fact that temporal databases are continually appended or updated. To address these problems, by integrating the advantages of these two algorithms, we extend the sequential pattern mining algorithm—PrefixSpan by introducing the concept of fuzzy temporal pattern and incremental learning, a new algorithm called Incremental sequential patterns for multivariate temporal association rules mining (ISPTAR) is proposed. First, the temporal transaction dataset obtained by fuzzy discretization of multivariate time series is converted into a temporal sequence dataset. Second, based on the PrefixSpan algorithm, the fuzzy temporal sequential patterns are mined by taking the valid time interval of the sequential pattern and the temporal relationship between items in the sequential pattern into account. Third, fuzzy temporal association rules can be constructed to mine the association between different attributes based on the mined fuzzy temporal sequential patterns. In addition, when new data are added, the proposed algorithm can update the sequential patterns without rescanning the historical database. We compare the ISPTAR algorithm with other sequential patterns mining and association rules mining algorithms on four real datasets from the UCI machine learning data repository. Experimental results demonstrate that ISPTAR, in most cases, outperformed the other algorithms in execution time and the number of generated rules. In addition, the rules generated by ISPTAR contain more temporal information, which is helpful to assist important decision-making.

A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

In real life, there exist a lot of attributed graphs each of which contains attribute information as well as structural information. As time goes on, a group of attributed graphs form an attributed graph sequence. Being the generalization of single-attributed graph sequences, multi-attributed graph sequences are arising vastly and quickly. Mining the temporal associations hidden in a multi-attributed graph sequence is in urgent need from data owners. To meet the need and fill the gap of research on mining such kind of temporal associations, we first give a definition of temporal association rules for describing temporal associations in a multi-attributed graph sequence, and then propose a fast algorithm for mining temporal association rules in a multi-attributed graph sequence which is based on the anti-monotonicity of support. The proposed algorithm is designed in two steps, namely finding frequent temporal association rules and verifying the credibility of these rules. Equipped with two novel joining and pruning strategies, the proposed algorithm exhibits much higher efficiency which is specially pursued in the process of rule mining. Experiments performed on synthetic datasets and real datasets show that the proposed algorithm is effective and more efficient than other existing algorithms.

Mining Temporal Association Rules with Temporal Soft Sets

Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.

A Hierarchical Energy Conservation Framework (HECF) of Wireless Sensor Networks by Temporal Association Rule Mining for Smart Buildings

The challenge of extending the sensor’s energy consumption is a key research issue in Wireless Sensor Networks (WSNs). Recently, association rule mining has proven to be a potential candidate to prolong the lifetime of sensor nodes in WSN. However, temporal correlations of the contextual values are not taken into account which is useful for the sensors to conserve their energy. Similarly, association rules mining at different tiers of the network has not been considered to reduce the number of transmission messages, by avoiding redundant data which is the major cause of the energy drain of sensors. In this paper, a novel Hierarchical Energy Conservation Framework (HECF) is proposed which aims to conserver’s energy at each layer of a network by using the Hierarchical Temporal Association Rule Mining in multistory buildings. In hierarchal setup, each floor of the building can conserve energy locally at the local sink and conserve entire network energy at the global sink by using temporal association rule mining at different tiers of the network. The HECF is ideal for large multistory buildings where energy conservation is a major issue along with effective monitoring and system performance. The result shows that HECF outperformed other classical energy conversation methods such as LEACH-C and RR-Schedule-Buffer in terms of energy consumption. It extends 16% network lifetime, also 20% less number of messages during data transmission, which is a remarkable improvement for sensors energy conservation.

Modified Ranking With Temporal Association Rule Mining in Supply Chains

This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules

The pervasive monitoring of HVAC systems through Building Energy Management Systems (BEMSs) is enabling the full exploitation of data-driven based methodologies for performing advanced energy management strategies. In this context, the implementation of Automated Fault Detection and Diagnosis (AFDD) based on collected operational data of Air Handling Units (AHUs) proved to be particularly effective to prevent anomalous running modes which can lead to significant energy waste over time and discomfort conditions in the built environment. The present work proposes a novel methodology for performing AFDD, based on both unsupervised and supervised data-driven methods tailored according to the operation of an AHU during transient and non-transient periods. The whole process is developed and tested on a sample of real data gathered from monitoring campaigns on two identical AHUs in the framework of the Research Project ASHRAE RP-1312. During the start-up period of operation, the methodology exploits Temporal Association Rules Mining (TARM) algorithm for an early detection of faults, while during non-transient period a number of classification models are developed for the identification of the deviation from the normal operation. The proposed methodology, conceived for quasi real-time implementation, proved to be capable of robustly and promptly identifying the presence of typical faults in AHUs.

Temporal Association Rule Mining and Updating and Their Application to Blast Furnace in the Steel Industry.

Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.

Temporal association rule mining: An overview considering the time variable as an integral or implied component

AbstractAssociation rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

AbstractIt is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.

Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

In retail business, customers’ behavior analytics is a study of customers’ buying behavior for a better understanding of customer needs to be able to provide service accordingly. The buying behavior is majorly influenced by the preferences of a customer. However, preferences of a customer change over a period of time due to various factors like change in income, taste, culture or newer products, etc. Understanding these changes in customer behavior is a very challenging task especially in a dynamic, ever-changing environment. There are various customer behavior mining models and techniques available in the data mining domain that are designed to work on static and dynamic databases. The traditional incremental mining techniques consider all the previous datasets in order to update the patterns. However, in a dynamic database, the size of the database grows with every update. To mine customers’ behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update. The purpose of this paper is to propose a method that can analyze the changes in customers’ behavior in time-variant databases without mining all the transactions. In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. The proposed algorithm named ‘Autoregressive Moving Average model-based Incremental Temporal Association Rules Mining (ARMA-ITARM)’ utilizes the ARMA model to substantially reduce the database and maintains temporal frequent patterns in the updated database. Inspired by sliding window and pre-large concepts, the algorithm utilizes past frequent itemsets and probable frequent itemsets from customers’ purchased history along with frequent itemsets and probable frequent itemsets that reduce search space. Consequently, the entire database is scanned only once to count the frequency of occurrence of a few candidate itemsets. In effect, execution time memory need of the algorithm is very small. Experimental results demonstrate that our proposed technique performs better over recent techniques like ITARM, SWF, etc.

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

An efficient algorithm for extracting appliance-time association using smart meter data

Demand Response (DR) programs play a significant role for developing energy management solutions. Gaining home residents trust and respecting their appliances usage preferences are essential factors for promoting these programs. Extracting resident's usage behaviour is a challenging task with the infinite massive amount of data being generated from smart meters. The main contribution of this paper is to extract temporal association patterns of energy consumption at appliance level. The proposed approach extends the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm to discover appliances usage preference at a time. The results achieved from the proposed work succeeded to discover appliance-time association considering appliances usage priority as a utility factor with respect to the 24-hours of the day as a temporal partitioning factor.