Rule Mining Problem Research Articles

Totally-ordered Sequential Rules for Utility Maximization

High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this article, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.

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.

New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches

The classification rule mining problem is one of the most important tasks of data mining. A constructed classification model is needed to have high accuracy, comprehensiveness, interestingness, etc. Furthermore, when the data composed of quantitative, numerical, or mixed data types, automatically discovering the appropriate intervals at the time of the mining process is a hard problem. The standard classification algorithms in the literature do not find the intervals of the quantitative attributes in the rules and this is performed a priori that causes the modification of datasets. Automatically constructing a successful classification model consisting of an explainable rule set without changing or modifying the data in artificial intelligence and machine learning is a very hot topic. Due to the philosophy of constantly researching to discover more accurate, surprising, and comprehensible rule sets and the absence of the most effective algorithm for all kinds of data sets, new methods or new versions of existing methods are proposed. In this study, automatic mining of high-quality rule set is handled as a multi-objective optimization problem due to the nature of the necessities and novel adaptive multi-objective intelligent search and optimization algorithms based on Grey Wolf Optimizer are proposed for this task. The datasets are considered as search spaces and the proposed adaptive Pareto based multi-objective Grey Wolf Optimizer algorithms are designed and applied as search methods for automatically discovering the high-quality rules. The proposed intelligent methods and successful classification algorithms such as naive Bayes, k-NN, support vector machines, decision trees, and RIPPER are tested in five real-world data sets with different characteristics. The obtained results show the efficiency of the proposed intelligent search and optimization methods.

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.

A survey on association rule mining based on evolutionary algorithms

ABSTRACT Searching for patterns in large database is one of the major tasks in data mining. This can be achieved by using association rule mining, which usually tends to find out the relations in an exhaustive manner. In real life, many data mining tasks require optimization between multiple objectives concurrently. Consequently over the years a large number of researches have been conducted on various techniques for efficient association rule mining and among them the field of evolutionary technique is growing rapidly with its large-scale applications and exceptional result. This research includes a systematical structured review of a wide range of state-of-the-art and recent multi-objective evolutionary algorithms (MOEAs) in terms of their chromosome representation, genetic operators and initial population, which are applied to categorical, quantitative and fuzzy rule mining problems. A lucid comparative study on various MOEA-based approaches includes computational complexity and applications within the context of this research. Finally, a guideline toward future studies on MOEA approaches has been presented that incorporates a general discussion of the current state of art literatures, newly rising study area (Interactive MOEA) along with their limitations and numerous possibilities.

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.

Evolving temporal association rules in recommender system

This research involves implementation of genetic network programming (GNP) and ant colony optimization (ACO) to solve the sequential rule mining problem for commercial recommendations in time-related transaction databases. Excellent recommender systems should be capable of detecting the customers’ preference in a proactive and efficient manner, which requires exploring customers’ potential needs with an accurate and timely approach. Due to the changing nature of customers’ preferences and the differences with the traditional find-all-then-prune approach, the interesting temporal association rules are extracted by the metaheuristics, genetic algorithms-based method of GNP. Additionally, a useful model is constructed using the obtained rules to forecast future customer needs and an ACO approach to evolve the online recommender system continuously. The methodology is experimentally evaluated in a real-world application by analysing the customer database of an online supermarket.

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.

RQL: A Query Language for Rule Discovery in Databases

Promoting declarative approaches in data mining is a long standing theme, the main idea being to simplify as much as possible the way data analysts interact with their data. This paper goes into this direction by proposing a well-founded logical query language, SafeRL, allowing the expression of a wide variety of rules to be discovered against a database. By rules, we mean statements of the form “if …then …”, as defined in logics for “implications” between boolean variables. As a consequence, SafeRL extends and generalizes functional dependencies to new and unexpected rules. We provide a query rewriting technique and a constructive proof of the main query equivalence theorem, leading to an efficient query processing technique. From SafeRL, we have devised RQL, a user-friendly SQL-like query language. We have shown how a tight integration can be performed on top of any relational database management system. Every RQL query turns out to be seen as a query processing problem, instead of a particular rule mining problem. This approach has been implemented and experimented on sensor network data. A web prototype has been released and is freely available (http://rql.insa-lyon.fr). Data analysts can upload a sample of their data, write their own RQL queries and get answers to know whether or not a rule holds (if not, a counterexample from the database is displayed) and much more.

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.