Quantitative Association Rule Mining Research Articles

Extended Association Rule Mining and Its Application to Software Engineering Data Sets

Association rule mining is a highly effective approach to data analysis for datasets of varying sizes, accommodating diverse feature values. Nevertheless, deriving practical rules from datasets with numerical variables presents a challenge, as these variables must be discretized beforehand. Quantitative association rule mining addresses this issue, allowing the extraction of valuable rules. This paper introduces an extension to quantitative association rules, incorporating a two-variable function in their consequent part. The use of correlation functions, statistical test functions, and error functions is also introduced. We illustrate the utility of this extension through three case studies employing software engineering datasets. In case study 1, we successfully pinpointed the conditions that result in either a high or low correlation between effort and software size, offering valuable insights for software project managers. In case study 2, we effectively identified the conditions that lead to a high or low correlation between the number of bugs and source lines of code, aiding in the formulation of software test planning strategies. In case study 3, we applied our approach to the two-step software effort estimation process, uncovering the conditions most likely to yield low effort estimation errors.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults.

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75-0.88) and lift (4.1-11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

Performance evaluation of machine learning for fault selection in power transmission lines

Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

A Discrete Crow Search Algorithm for Mining Quantitative Association Rules

Association rules are the specific data mining methods aiming to discover explicit relations between the different attributes in a large dataset. However, in reality, several datasets may contain both numeric and categorical attributes. Recently, many meta-heuristic algorithms that mimic the nature are developed for solving continuous problems. This article proposes a new algorithm, DCSA-QAR, for mining quantitative association rules based on crow search algorithm (CSA). To accomplish this, new operators are defined to increase the ability to explore the searching space and ensure the transition from the continuous to the discrete version of CSA. Moreover, a new discretization algorithm is adopted for numerical attributes taking into account dependencies probably that exist between attributes. Finally, to evaluate the performance, DCSA-QAR is compared with particle swarm optimization and mono and multi-objective evolutionary approaches for mining association rules. The results obtained over real-world datasets show the outstanding performance of DCSA-QAR in terms of quality measures.

An Interval Type-2 Fuzzy Association Rule Mining Approach to Pattern Discovery in Breast Cancer Dataset

In the literature, several methods explored to analyze breast cancer dataset have failed to sufficiently handle quantitative attribute sharp boundary problem to resolve inter and intra uncertainties in breast cancer dataset analysis. In this study an Interval Type-2 fuzzy association rule mining approach is proposed for pattern discovery in breast cancer dataset. In the first part of this analysis, the interval Type-2 fuzzification of the breast cancer dataset is carried out using Hao and Mendel approach. In the second part, FP-growth algorithm is adopted for associative pattern discovery from the fuzzified dataset from the first part. To define the intuitive words for breast cancer determinant factors and expert data interval, thirty (30) medical experts from specialized hospitals were consulted through questionnaire poling method. To establish the adequacy of the linguistic word defined by the expert, Jaccard similarity measure is used. This analysis is able to discover associative rules with minimum number of symptoms at confidence values as high as 91%. It also identifies High Bare Nuclei and High Uniformity of Cell Shape as strong determinant factors for diagnosing breast cancer. The proposed approach performed better in terms of rules generated when compared with traditional quantitative association rule mining. It is able to eliminate redundant rules which reduce the number of generated rules by 39.5% and memory usage by 22.6%. The discovered rules are viable in building a comprehensive and compact expert driven knowledge�base for breast cancer decision support or expert system

QuantMiner for mining quantitative association rules

In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers good intervals in association rules by...

Association Rule Mining for Localizing Solar Power in Different Distribution Grid Feeders

The increase in the photovoltaic generation on distribution grids may create problems, such as voltage-violations. To gain situational awareness for system operation, e.g., adjusting the tap-settings of the transformers or adjust capacitor banks, utilities need situational awareness about locations and amounts of photovoltaic powers being generated in different feeders. Unfortunately, many utilities not only lack observability of the distribution grid, e.g., no secondary grid schematics but also have no situational awareness on which feeders solar panels locate. To understand where the solar users are roughly, we propose to use the feeder measurements from utilities with solar panel measurements from third-party solar companies. Due to the property of active correlation detection, we propose several sequentially improved methods based on quantitative association rule mining (QARM), where we also provide a lower bound for performance guarantees based on the amount of available data and the size of the bin for clustering. However, the binning of data leads to information loss. So, we design a band to replace bin for creating a new data mining approach for robustness. We validate our result for the IEEE 4-, 8-, 123-, 8500-bus cases with the Pecan-Street dataset, and also for the IEEE 123-bus case under low/high penetration and with radial/weakly meshed configurations. For realistic validation, we also obtain real data from a utility and a solar power company in the same zip codes in a city of California. Numerical results show accurate associations of feeders and solar panels, leading to increased situational awareness of the secondary distribution grids.

Data mining of solar panel data to analyze the effects of different metals in producing photocurrent

Solar panels are photosensitive devices that generate electric current by being exposed to radiant power. The “Solar Army” research group at FSU’s Department of Chemistry has been collecting solar panel data. These data consist of readings from plates that contain oxides of various metallic elements. Each of these plates is represented by a 6x6 grid of cells, where the leftmost and rightmost cells are known as standards -- typically containing copper and iron respectively. The original data has been preprocessed to represent the data for one of the 6x6 cells of a plate with one data record, including the type of electric lead used, the amount of photocurrent, and the amount of fifteen different metals present on the cell. The goal of this work was to analyze the effects different metals in generating photo current so that the effectiveness of the solar panels can be improved. We used the data mining tool Weka to preprocess and apply class association rules (CAR) mining to discover the combination metals that can produce different intensities of photo current. We also applied quantitative association rule mining to determine if the quantity of metals present can affect the photo current. These test results may be important not only to the Solar Army research group, but also to manufactures and scientists attempting to increase the effectiveness of solar panels.

Sensitivity Analysis of Non-Dominated Sorting Genetic Algorithm-II for Quantitative Association Rules Mining

İkili ya da kesikli değerlere sahip veri kümelerine odaklanan birçok birliktelik kural madenciliği çalışması vardır. Ancak, gerçek dünya uygulamalarındaki veriler genellikle nicel değerlerden oluşmaktadır. Nicel veriler için keşfedilecek kurallarda hangi niteliklerin olacağı ve hangilerinin kuralın solunda hangilerinin sağında olacağının belirlenmesi, ilgili nicel aralıkların en uygun şekilde otomatik ayarlanması; kuralların yoğun nesne kümeleri üretilmeden tek aşamada anlaşılabilir, doğru, güvenilir, ilginç, sürpriz vb. özelliklere sahip olacak şekilde bulunması ve tüm bu işlemlerin her veri tabanı için önceden belirlenmesi gereken metriklere ihtiyaç duyulmadan ayarlanması zor bir problemdir. Yakın zamanda bazı araştırmacılar, nicel birliktelik kural madenciliğini, farklı kriterleri aynı anda en iyi şekilde karşılayacak şekilde, çok amaçlı bir problem olarak düşünmüşlerdir. Bu makalede nicel birliktelik kural madenciliği problemi için anlaşılabilirlik, ilginçlik ve performansı en üst düzeye çıkarmayı amaçlayan çok amaçlı evrimsel algoritmalardan baskın olmayan sıralama genetik algoritma-II temelli QAR-CIP-NSGA-II’nin parametre analizi yapılmıştır. Bu amaçla; nitelikleri nicel değerler alan beş gerçek dünya verisinde QAR-CIP-NSGA-II’nin değerlendirme sayısı, popülasyon sayısı, mutasyon olasılığı, genlik ve eşik değeri gibi parametrelerinin; elde edilen kural sayısı, ortalama destek, güven, lift, kesinlik faktörü, netconf ve kapsanan kayıt sayısını nasıl değiştirdiği kapsamlı bir şekilde bildiğimiz kadarıyla ilk kez bu çalışmada gerçekleştirilmiştir. Detaylı analiz sonuçları karşılaştırmalı tablolar ile sunulmuştur ve yorumlanmıştır.

Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design

In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms.

A novel hybrid GA–PSO framework for mining quantitative association rules

Discovering association rules is a useful and common technique for data mining in which dependencies among datasets are shown. Discovering the rules from continuous numeric datasets is one of the common challenges in data mining. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the criteria of support and confidence. By drawing on two heuristic optimization techniques, to wit, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, a hybrid algorithm for extracting quantitative association rules was developed in this research. Accurate and interpretable rules result from the integration of the multiple objectives GA with the multiple objective PSO algorithms, which redresses the balance in the exploitation and exploration tasks. The useful and appropriate rules and the most suitable numerical intervals are discovered by proposing a multi-criteria method in which there is no need to discretize numerical values and to determine threshold values of minimum support and confidence. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness and comprehensibility. The results gained over five real-world datasets evidence the effectiveness of the proposed method. By hybridization of the GA and the PSO algorithm, the proposed approach has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage.

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Discovering association rules is a useful and common technique for data mining in which relations and co-dependencies between datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the Genetic Algorithm, and there is no need there is no need to determine the extent of the threshold for the support and confidence criteria. By proposing a multi-criteria method, the useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discrete numerical values and the determination of the minimum support threshold and minimum confidence threshold. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness, and cosine2. The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets and the results show the performance superiority of the proposed algorithm compared to similar algorithms.

PPQAR: Parallel PSO for quantitative association rule mining

Mining quantitative association rules is one of the most important tasks in data mining and exists in many real-world problems. Many researches have proved that particle swarm optimization(PSO) algorithm is suitable for quantitative association rule mining (ARM) and there are many successful cases in different fields. However, the method becomes inefficient even unavailable on huge datasets. This paper proposes a parallel PSO for quantitative association rule mining(PPQAR). The parallel algorithm designs two methods, particle-oriented and data-oriented parallelization, to fit different application scenarios. Experiments were conducted to evaluate these two methods. Results show that particle-oriented parallelization has a higher speedup, and data-oriented method is more general on large datasets.

Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

Cellular network performance is often evaluated by key performance indicator (KPI) and key quality indicator (KQI). The association between KQI and KPI is the most critical step to optimize the performance of cellular network. Traditional association methods between KPI and KQI are based on the end-to-end evaluation. However, these methods require professional engineers to evaluate cellular network performance, and the man-manned evaluation is often inaccurate and labor-consuming, which cannot find out the caused factor of deterioration networks. In order to solve the problem, we propose a machine learning based quantitative association rule mining (QARM) method called SWP-RF, which consists of sliding-window partitioning (SWP) and random forest (RF), to associate KPI with KQI. Specifically, we first use SWP to discretize continuous attributes into boolean values, which is adopted to mine its association rules. The warning intervals and the warning points is obtained by SWP. Next, we use RF feature importance to measure the association between KPI and KQI. The priority of the association strength between all KPIs and each KQI can be obtained by RF. Finally, we select the warning points and the association strength priority as optimal output solution. Experiments are conducted over the actual data from telecom operators and the results confirm the feasibility and accuracy of the proposed method.

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles.

In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the Artificial Bee Colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bio-inspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparently (to the best of our knowledge) several new genes were discovered that have not been seen in any past studies. For the applicability demand, based on the results acqured from microarray gene expression analysis, we can conclude that DQB can be adopted in a different real world applications with some modifications.

Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean

El Niño–Southern Oscillation (ENSO) and its relationships with marine environmental parameters comprise a very complicated and interrelated system. Traditional spatiotemporal techniques face great challenges in dealing with which, how, and where the marine environmental parameters in different zones help to drive, and respond to, ENSO events. Remote sensing products covering a 15-year period from 1998 to 2012 were used to quantitatively explore these patterns in the Pacific Ocean (PO) by a prevail quantitative association rule mining algorithm, that is, a priori, within a mining framework. The marine environmental parameters considered were monthly anomaly of sea surface chlorophyll-a (CHLA), monthly anomaly of sea surface temperature (SSTA), monthly anomaly of sea level anomaly (SLAA), monthly anomaly of sea surface precipitation (SSPA), and monthly anomaly of sea surface wind speed (WSA). Four significant discoveries are found, namely: (1) Association patterns among marine environmental parameters and ENSO events were found primarily in five sub-regions of the PO: the western PO, the central and eastern tropical PO, the middle of the northern subtropical PO, offshore of the California coast, and the southern PO; (2) In the western and the middle and east of the equatorial PO, the association patterns are more complicated than other regions; (3) The following factors were found to be predicators of and responses to La Niña events: abnormal decrease of SLAA and WSA in the east of the equatorial PO, abnormal decrease of SSPA and WSA in the middle of the equatorial PO, abnormal decrease of SSTA in the eastern and central tropical PO, and abnormal increase of SLAA in the western PO; (4) Only abnormal decrease of CHLA in the middle of the equatorial PO was found to be a predicator of and response to El Niño events. These findings will help to improve our abilities to identify the marine association patterns in factors relating to ENSO events.

An FCA approach to mining quantitative association rules from multi-relational data

In this paper, we propose an algorithm for mining quantitative association rules (ARs) from a multi-relational database (MRDB). A MRDB contains multiple tables (relations), and attributes in a table are either categorical or quantitative (or numerical). To handle numerical data in a pattern, we consider (logical) conjunctions with interval constraints, using the notion of closed interval patterns (CIPs) proposed by Kaytoue et al. in formal concept analysis (FCA). We then present an algorithm for mining strong quantitative ARs, namely they satisfy both a minimum support and a minimum confidence. We also propose a pruning method tailored to computing CIPs in an AR. We give some experimental results, which show the effectiveness of the proposed method, compared with the conventional methods such as a discretisation-based approach or an optimisation-based approach.

An FCA approach to mining quantitative association rules from multi-relational data

An FCA approach to mining quantitative association rules from multi-relational data

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets

Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of computational costs without losing quality in the results when the proposed techniques are applied.