Association Rule Mining Problem Research Articles

Condensed Representations of Association Rules in n-ary Relations

The problem of association rules mining has given rise to a rich literature, especially in classic binary bidimensional data. In particular, the representation of the set of rules without loss of information is well understood. This is not the case in multidimensional binary data. This paper shows that the knowledge of n-1 components of every closed n-sets of a multidimensional Boolean tensor, as well as the cardinality of the remaining dimension, is enough to allow for the derivation of the confidence of every multidimensional association rule. This generalises well-known results in the bidimensional case. This paper provides experimental comparisons between the numbers of closed n-sets and frequent associations.

Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm

Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.

Extracting method of packet dependence from NoC simulation traces using association rule mining

Networks on-chip (NoCs) interconnect complex parallel applications on multiprocessors systems-on-chip. In order to rapidly evaluate NoCs, designers replace processing elements by communication traces on simulations. However, trace-based simulations have low accuracy due to the lack of information about packet dependence. The methods to obtain packet dependence require running multiple simulations or modifying application and simulator source code to output dependence; both are very costly. In this paper, we model packet dependence extraction as an association rule mining problem. We use the Apriori algorithm to extract communication patterns using traces from only one full-system simulation. The experiments with real, synthetic, and self-similar traffic show about 78% of packet dependence accuracy.

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.

Secure Mining of Association Rules in Distributed Datasets

The arrival of Information Age, with its rapid development of information technology, has provided a wide space for Data Analysis and Mining. Yet growth in this market could be held back by privacy concerns. This paper addresses the problem of secure association rule mining where transactions are distributed across sources. The existing solutions for distributed data (vertical partition and horizontal partition) have high complexity of encryption and incomplete definition of attributes of multiple parties. In this paper, we study how to maintain differential privacy in distributed databases for mining of association rules without revealing each party's raw transactions despite how strong background knowledge the attackers have. We use a intermediate server for data consolidation without assuming it is safe. Our methods offer enhanced privacy against various attacks model. In addition, it is simpler and is significantly more efficient in terms of communication rounds and computation overhead.

Privacy Preserving Distributed Association Rule Mining Approach on Vertically Partitioned Healthcare Data

The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost.

Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases

This paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, it requires huge computational resource when considering big databases for mining. To overcome this limitation, we develop in this paper a GPU-based Bees Swarm Optimization Miner (GBSO-Miner) where the GPU is used as a co-processor to compute the CPU-time intensive steps of the algorithm. Unlike state-of-the-art GPU-based ARM methods, all BSO steps including the determination of search area, the local search, the evaluation, and the dancing are performed on GPU. A mapping method between the data input of each task and the GPU blocks/threads is developed. To demonstrate the effectiveness of the GBSO-Miner framework, intensive experiments have been carried out. The results show that GBSO-Miner outperforms the baseline methods of the literature (GPApriroi, MEGPU, and Dmine) using big textual and graph databases. The results reveal that GBSO-Miner is up to 800 times faster than an optimized CPU-Implementation.

DISCOVERY AND ANALYSIS OF JOB ELIGIBILITY AS ASSOCIATION RULES BY APRIORI ALGORITHM

Association rule mining is a data mining technique in which pattern of occurrences of one set of items with another set of items in databases of transactions are discovered as rules of implication with certain measures of interestingness. Support or the frequency of occurrences of sets of items and confidence are the most widely used measures of interestingness of association rules of the form X→Y where X and Y are disjoint sets of items. Though the problem of association rule mining emerged from analysis of market basket data in supermarket there are numerous areas of applications of association rule mining technique. In this paper, association rule mining method is applied to discover and analyze eligibility criteria for jobs from a large set of data for choosing career and professional goals effectively. For this the data are collected by conducting a wide survey and is prepared and modeled suitably. Then the a priori algorithm is implemented for discovering the frequent itemsets and the association rules. The discovered rules are then classified based on the kind of jobs and also based on the kinds of qualifications. The discovered results are analyzed and interpreted and the computational performances are also analyzed.

How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem

The application of population-based metaheuristics approaches to the association rules mining problem is explored in this paper. The combination of GPU and cluster-based parallel computing techniques is investigated for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. We propose four parallel-based approaches that benefit from the cluster intensive computing in the generation process and the massively GPU threading. This is by evaluating the association rules in parallel on GPU. To validate the proposed approaches, the most used population-based metaheuristics (GA, PSO, and BSO) have been executed on a cluster of GPUs to solve benchmarks of large and big ARM instances. We used Intel Xeon 64bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that the BSO outperforms GA and PSO. They also show that the proposed solution outperforms the HPC-based ARM approaches when exploring Webdocs instance (the largest instance existing on the web). To our knowledge, this is the first work that explores the combination of GPU and cluster-based parallel computing with the population-based metaheuristics in association rule mining.

Multi-objective bat algorithm for mining numerical association rules

Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases. Generally, ARM is more complex when we introduce the quality criteria and the usefulness to the user. In this paper deals with the problem of numerical ARM. In which, we propose a new multi-objective meta-heuristic called multi-objective bat algorithm for association rules mining (MOB-ARM). To identify more useful and understandable rules, we introduce four quality measures of association rules: Support, confidence, comprehensibility, and interestingness, in two objective functions. A series of experiments are carried out on several well-known benchmarks in ARM field and the performance of our proposal are evaluated and compared with those of other recently published methods including mono-objective and multi-objective approaches. Also, the paper presents a comparative study with three other methods dealing with multi-objective association rule mining. The obtained results show that our method is competitive with other methods and extract useful and understandable rules.

Multi-objective bat algorithm for mining numerical association rules

Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases. Generally, ARM is more complex when we introduce the quality criteria and the usefulness to the user. In this paper deals with the problem of numerical ARM. In which, we propose a new multi-objective meta-heuristic called multi-objective bat algorithm for association rules mining (MOB-ARM). To identify more useful and understandable rules, we introduce four quality measures of association rules: Support, confidence, comprehensibility, and interestingness, in two objective functions. A series of experiments are carried out on several well-known benchmarks in ARM field and the performance of our proposal are evaluated and compared with those of other recently published methods including mono-objective and multi-objective approaches. Also, the paper presents a comparative study with three other methods dealing with multi-objective association rule mining. The obtained results show that our method is competitive with other methods and extract useful and understandable rules.

Discovering co-location patterns with aggregated spatial transactions and dependency rules

Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with non-overlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to find such associations from real datasets. The discovered co-location patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.

The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm

The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of PSO and Cauchy distribution because this performance proved to reach the optimal rules. In this paper, we focus to adopt the combination for solving association rule mining (ARM) optimization problem in numerical dataset. Therefore, the aim of this research is to extract the rule of numerical ARM optimization problem for certain multi-objective functions such as support, confidence, and amplitude. This method is called PARCD. It means that PSO for numerical association rule mining problem with Cauchy Distribution. PARCD performed better results than other methods such as MOPAR, MODENAR, GAR, MOGAR and RPSOA.

An Optimized and Secure Association Rule Mining Algorithm in Parallel and Distributed Environment

Data Mining is a subfield of Artificial Intelligence which is mainly used for extraction of hidden predictive information from large available databases. To Handle Large Data set is very tedious and complex task. The solution to this problem is to apply distributed or parallel approaches. The field of distributed data mining has therefore gained increasing importance in the last decade. In Distributed data mining Security is the major problem with respect to association rule mining projects. Association rule mining is become one of the core data mining tasks and has attracted tremendous interest among data mining Researchers. The Apriori algorithm by Rakesh Agarwal has emerged as one of the best Association Rule mining algorithms. It also serves as the base algorithm for most parallel algorithms. The performance of data mining algorithm can be accelerated from O(N) to O(N/k) with parallelism, where N = number of data records and k =number of nodes in distributed system. By using various cryptographic techniques and Method interesting associations and patterns between variables of big database can be observed securely. This system addresses the problem of Secure association rule mining over the horizontally distributed database system. In Current technique for association rule mining huge processing time and also high threat from various attacks. The proposed system can provide security at the time of mining data from various data sources and also reduce computation time by using parallel programming environment like OpenMp. The goal of proposed approach is to find all association rule with maintain support s and confidence c and minimize the information disclosed about the private databases held by those player .This system is based on distributed mining algorithm, K&C and AES algorithm. Distributed mining algorithm used here is the distributed version of apriori algorithm. With proposed approach speed up is acquired using Parallel Computing while preserving the privacy of the data .

Reducing thread divergence in GPU‐based bees swarm optimization applied to association rule mining

SummaryThe association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU‐based bees swarm optimization metaheuristic for ARM, respectively, named block‐based reordering, transactions‐based reordering, and transactions‐based reordering with median value. Theoretical and experimental studies have been carried out using well‐known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad‐core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster. Copyright © 2016 John Wiley & Sons, Ltd.

Discovering useful patterns from multiple instance data

Association rule mining is one of the most common data mining techniques used to identify and describe interesting relationships between patterns from large datasets, the frequency of an association being defined as the number of transactions that it satisfies. In situations where each transaction includes an undetermined number of instances (customers shopping habits where each transaction represents a different customer having a varied number of instances), the problem cannot be described as a traditional association rule mining problem. The aim of this work is to discover robust and useful patterns from multiple instance datasets, that is, datasets where each transaction may include an undetermined number of instances. We propose a new problem formulation in the data mining framework: multiple-instance association rule mining. The problem definition, an algorithm to tackle the problem, the application fields, and the relations’ quality measures are formally described. Experimental results reveal the scalability of the problem on different data dimensionality. Finally, we apply it to two real-world applications field: (1) analysis of financial data gathered from one of the most important banks in Lithuania; (2) study of existing relations between records of unemployed gathered from the Spanish public employment service.

On the mining of fuzzy association rule using multi-objective genetic algorithms

The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support and confidence is introduced as an alternative tool. Besides, rule length, comprehensibility, and interestingness are also potentially used as quality metrics. Additionally, in fuzzy association rule mining, determining number fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence fuzzy association rule mining problem can be viewed as a multi-objective optimisation problem. On the other side, multi-objective genetic algorithms are established and efficient techniques to uncover Pareto front. Therefore, to bridge these two fields of research many methods have been developed. In this paper, we present some of the popular state-of-art multi-objective fuzzy-genetic algorithms for mining association rules. In addition, their novelty, strengths, and weaknesses have been analysed properly with a comparative performance. The indicative future research direction and an extensive bibliography of this paper may be an attracting point for researchers from diversified domains to explore and exploit further.

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.

An Efficient Algorithm for Mining Coherent Association Rules

are many data mining techniques for finding association rules with the predefined minimum support from transaction databases. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules convey common-sense knowledge. Mining association rules without minimum support threshold is an important approximation of the association rule mining problem, which has been recently proposed(1).In this approach rules are generated based on logical implications and hence fewer and more meaningful rules called coherent rules are reported. An algorithm named ChSearch, is proposed to mine coherent rules for the user specified itemset. In this paper, the same problem is studied with two additional improvements. First, Apriori-based algorithm, namely ECARM for mining all coherent rules is proposed. Second to speed up time consuming candidate generation process an efficient pruning strategy is introduced. The proposed method generates coherent rules in shorter average execution times and fewer database scans, and thereby reduce great amount of I/O time per database scan and hence scaled to large databases. The method has been evaluated using both synthetic and real datasets, and the experimental results demonstrate its effectiveness and efficiency.