Association Rule Mining Process Research Articles

A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains.

User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilized for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.

Current situation and training strategies of preschool students’ skills in the Internet era

Abstract The analysis of the current situation of skills and training strategies of preschool students in colleges and universities is to provide more professional talents for the cause of early childhood education. This paper first gives a brief description of the association rule mining algorithm under Internet technology, gives the evaluation method of association rules, including support, confidence, and enhancement, and gives the basic process of association rule mining. Next, it is explained that the multi-objective optimization problem utilizes the objective function optimization for calculating the optimal solution, and the commonly used multi-objective optimization problem solution and the selection method of the fitness function are given. Then the principle of the whale optimization algorithm is described and explained, the whale optimization algorithm is used to optimize the objective function of association rules, and then the steps and processes of the WOA-ARM algorithm are obtained. Finally, the algorithm was used to mine the training strategy of GZ University as an example, the “situational experience” teaching and training method was proposed, and the teaching characteristics and objectives of this method were analyzed. Regarding teaching objectives, 48.75%, 32.27%, 12.39%, and 6.59% of the ratings were A, B, C, and D, respectively. This indicates that the “situational experience” teaching training is suitable for college preschool students, and there is a strong correlation between the current skills and the training strategy.

Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloud

As one of the important ways in data mining, the association rule mining is to analyze the correlation of transactions based on massive data and mine the hidden valuable information. However, excessive data collection and analysis might lead to the privacy leakage of user data and the damage of data integrity. Meanwhile, in the existing privacy-preserving schemes, the cloud servers such as the evaluator have strong decryption capabilities, which causes active attacks easily, or data reliability in mining and analysis is not considered. In response to these problems, this paper proposes a verifiable privacy-preserving association rule mining scheme (VPPARM) using distributed decryption mechanism on the cloud. First, the scheme adopts distributed decryption to complete the data mining tasks through the dual-cloud servers, which weakens the decryption ability of the cloud servers and prevents the server from active attacks. Secondly, to protect the privacy of the association rule mining process, our scheme adopts adding virtual transactions, permutations, and random number masking collaboratively to hide the data in the whole mining process. In addition, to eliminate the hidden danger of illegal users, our scheme adopts a verifiable short digital signature scheme to verify data integrity and ensure data reliability, avoiding the poisoning attack. Finally, through the performance evaluation, the results demonstrate that our scheme realizes correctness, security, reliability with lower communication and computation costs and improves efficiency to a certain extent.

Modeling of the English Translation Complex System Based on Discrete Data Mining

This paper provides an in-depth analysis and research on the application of web discrete data mining in modeling complex systems for English translation. This paper adopts association rule mining, clustering, and classification methods to deeply analyze English translation, mine the interesting rules in it, find out the interinfluence relationship of system modeling and the relationship between translation behaviors, analyze and compare different data mining methods, and make an evaluation of the mining results to provide a scientific reference basis for the relevant decision-making scheme of the English translation. The paper elaborates the relevant concepts of data mining and the theoretical basis and analysis process of association rule mining, clustering rule mining, and classification rule mining methods. The application of data mining techniques in modeling and analysis of complex systems of the English translation is highlighted, and these three mining methods are used to analyze a set of English sample data: through the analysis of the correlation of different English translations, some feasible suggestions are provided for the order of translation settings; through the analysis of clustering of English translations, the shortcomings of traditional translation methods are made up; through the analysis of the factors affecting the accuracy of English translations factors, a useful reference is provided on how to improve English translation and then improve the quality of translation.

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.

Risk prediction and early warning for air traffic controllers’ unsafe acts using association rule mining and random forest

In the context of big data, scientific judgment of the future trend or state of unsafe acts of air traffic controllers (ATCers) plays an important role in the prevention of unsafe incidents. Therefore, based on the previous prediction and early warning studies, this study chose the methods of correlation analysis and nonlinear modeling to scientifically predict and warn ATCers’ unsafe acts. Firstly, according to the process of association rule mining, the association rules among risk factors and controllers' unsafe acts were mined. On the basis of association rule mining, the prediction and early warning model was established by random forest algorithm. The prediction data samples were used to predict ATCers' errors and violations respectively. The results show that the number of two-dimensional association rules is the largest, followed by one-dimensional and two-dimensional association rules, and four-dimensional association rules is the least. For the prediction of errors and violations, the deviation between the predicted value and actual value is small, indicating that the prediction accuracy is high. Business ability, technical environment, inadequate supervision and mental states are of greater importance to the prediction of errors, while inadequate supervision, organizational climate, organizational process and mental states are more important for the prediction of violations. The application of association rule and random forest model can effectively predict ATCers' errors and violations, and send signals to relevant departments according to the threshold and alarm level to report dangerous situations, thus reducing the losses caused by ATCers' unsafe acts.

PRUCARS: improved association rule-based social recommender systems using overlapping community detection

Abstract With the increase of information on the web and online users’ activities, to find the appropriate information at the right time and discover items that customers are interested in among the available choices become difficult challenges. Recommender Systems have been introduced to overcome this problem by offering potentially relevant elements and providing users with personalized recommendations. Although several social-based recommendation techniques have been proposed in the literature, applying the community detection techniques based on previous transactions of users can improve the precision of the algorithms and better handle the data sparsity and cold-start problem. In this paper, we propose an improved association rule mining process based on Rule Power Factor to generate potent rules and enhance the accuracy of recommendation. The performance of the algorithm is analyzed on transactional datasets of MovieLens. The experimental results show that our method outperforms several state-of-the-art recommendation methods with increased precision, accuracy and minimum Mean Average Error values.

Association Rule Hiding in Privacy Preserving Data Mining

This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.

A haoop-based parallel mining of frequent itemsets using N-Lists

Frequent pattern mining is the most important phase of association rule mining process because of its time and space complexity. Several methods have attempted to improve the performance of association rule mining by enhancing frequent pattern mining efficiency. Due to the large size of the data-sets and huge amounts of data which should be mined, many parallel and distributed mining approaches have been introduced to divide data-sets or to distribute mining processes between multiple processors or computers and thus, improve the efficiency of the mining process. In this paper, we propose a hadoop-based parallel implementation of PrePost+ algorithm for frequent itemset mining. In our parallel approach, the process of constructing N-Lists of itemsets has been distributed between the mappers and the operation of the final pruning process and extracting frequent itemsets has been carried out by reducers in a map-reduce parallel programming model. The experimental results show that our hadoop-based PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel methods of frequent itemset mining (PARMA) in terms of execution time.

FP-GROWTH ALGORITHM BASED INCREMENTAL ASSOCIATION RULE MINING ALGORITHM FOR BIG DATA

Discovering associations among huge collection of transactions is beneficial to rectify and to take appropriate decision made by decision makers. Discovering frequent itemsets is the key process in association rule mining. Since association rule mining process generates large number of rules which makes the algorithm inefficient is the biggest challenge for any and makes it difficult for the end users to comprehend the generated rules. The better idea is to use iterative technique to discover association rules. To overcome this problem, incremental updating of frequent itemsets is proposed in this paper. Proposed incremental data mining algorithm is based on FP-Growth and uses the concept of heap tree to address the issue of incremental updating of frequent itemsets. The proposed uses good tricks of FP-Growth, and significantly reduces the complexity. The experimental results show that the proposed algorithm reduces the execution time substantially and outperforms other algorithms.

Privacy preserving association rule mining based on homomorphic computations

Privacy-preserving data mining (PPDM) techniques allow the knowledge extraction from data, while privacy preserving in data mining applications. Many of the researchers have recently made an effort to preserve privacy of sensitive knowledge or information in a real database. Association rule mining and frequent item-set mining are two popular and widely studied data analysis techniques for a range of applications. To ensure data privacy, in this paper, we design an efficient homomorphic encryption-based scheme for privacy preserving data mining. The main issues with some of the known privacy preserving methods are - high computational complexity and large communication cost required for their execution. Our methods provide perfect secrecy and resist various attacks to some extent in association rule mining process. We presented correctness, security analysis and experimental results for the proposed system. We also presented the comparison of our proposed method with other significant state of the art methods.

Privacy preserving association rule mining based on homomorphic computations

Privacy-preserving data mining (PPDM) techniques allow the knowledge extraction from data, while privacy preserving in data mining applications. Many of the researchers have recently made an effort to preserve privacy of sensitive knowledge or information in a real database. Association rule mining and frequent item-set mining are two popular and widely studied data analysis techniques for a range of applications. To ensure data privacy, in this paper, we design an efficient homomorphic encryption-based scheme for privacy preserving data mining. The main issues with some of the known privacy preserving methods are - high computational complexity and large communication cost required for their execution. Our methods provide perfect secrecy and resist various attacks to some extent in association rule mining process. We presented correctness, security analysis and experimental results for the proposed system. We also presented the comparison of our proposed method with other significant state of the art methods.

Coalesce based binary table: an enhanced algorithm for mining frequent patterns

Frequent item set mining and association rule mining is the key tasks in knowledge discovery process. Various customized algorithms are being implemented in Association Rule Mining process to find the set of frequent patterns. Though we have many algorithms apriori is one of the standard algorithm for finding frequent itemsets, but this algorithm is inefficient because of several scans of database and more number of candidates to be generated. To overcome these limitations, in this paper a new algorithm called Coalesce based Binary Table is introduced. Through this algorithm the given database is scanned only once to generate Binary Table by which frequent-1 itemsets are found. To progress the process, infrequent-1 itemsets are identified and removed from the Binary Table to rearrange the items in support ascending order. To each frequent-1 itemset find Coalesce matrix and Index List to generate all frequent itemsets having the same support count as representative items and the remaining frequent itemsets are obtained in depth first manner. The significant benefits with the proposed method are the whole database is scanned only once, no need to generate and check each candidate to find the set of frequent items. On the other hand frequent items having the same support counts as representative items can be identified directly by joining the representative item with all the combinations of Coalesce matrix. So, it is proven that coalesce based Binary Table is panacea to cut short the time in identifying the frequent itemsets hence the efficiency is improved.

Visual analysis of user-driven association rule mining

Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A hierarchical matrix-based visualization technique is proposed for the user to explore the measure value and the intermediate results of association rules. We also design a set of visual exploration tools to support interactively inspection and manipulation of mining process. The effectiveness and usability of our approach is demonstrated with two scenarios.

Incremental topological spatial association rule mining and clustering from geographical datasets using probabilistic approach

Due to the dynamic updating of real time spatial databases, the preservation of spatial association rules for dynamic database is a vital issue because the updates may not only invalidate some existing rules but also make other rules relevant. Consequently, the dynamic updating of spatial rules was handled by many researchers through the incremental association rule mining algorithm. Accordingly, in this paper we have developed an incremental topological association rule mining of geographical datasets using probabilistic approach. Initially, the spatial database is read out and it is passed through probability-based incremental association rule discovery algorithm to mine the topological spatial association rules. Once the rules are mined from the spatial database, the assumption here is that the database is dynamically updating for every time interval. In order to handle this dynamic nature, the proposed incremental topological association rule mining process is used in this paper. Here, the candidate topological rule generation is done from the spatial association rules using the topological relations such as, nearby, disjoint, intersects and inside/outside and the topological support is calculated using the proposed probabilistic topological support model. Finally, the spatial clustering is performed based on the mined spatial rules. From the experimentation, we proved that the maximum accuracy reached by the proposed method is 83.14% which is higher than the existing methods, which is defined as the ratio of the occurred rules and total number of topological data objects.

Speeding-Up Association Rule Mining With Inverted Index Compression.

The growing interest in data storage has made the data size to be exponentially increased, hampering the process of knowledge discovery from these large volumes of high-dimensional and heterogeneous data. In recent years, many efficient algorithms for mining data associations have been proposed, facing up time and main memory requirements. Nevertheless, this mining process could still become hard when the number of items and records is extremely high. In this paper, the goal is not to propose new efficient algorithms but a new data structure that could be used by a variety of existing algorithms without modifying its original schema. Thus, our aim is to speed up the association rule mining process regardless the algorithm used to this end, enabling the performance of efficient implementations to be enhanced. The structure simplifies, reorganizes, and speeds up the data access by sorting data by means of a shuffling strategy based on the hamming distance, which achieve similar values to be closer, and considering both an inverted index mapping and a run length encoding compression. In the experimental study, we explore the bounds of the algorithms' performance by using a wide number of data sets that comprise either thousands or millions of both items and records. The results demonstrate the utility of the proposed data structure in enhancing the algorithms' runtime orders of magnitude, and substantially reducing both the auxiliary and the main memory requirements.

AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.

Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules

Since finding quantitative association rules (QARs) is an NP-hard problem, evolutionary methods are suitable solutions for discovery QARs. Nevertheless, most of the previous evolutionary methods to discover association rules only consider frequent dependency among items in datasets. They do not pay specific attention to interestingness and non-redundancy as two critical objectives. In this paper, the proposed algorithm (Rare-PEARs) gives a chance to each rule with different length and appearance (antecedent and consequent parts of rules) to be created. Therefore, various interesting, rare or interesting and rare rules can be found. Some of these rules might be uninteresting (those that contain frequent item sets). However, we try to avoid them by Rare-PEARs. To accomplish this goal, our method decomposes the process of association rule mining into N−1 sub-problems (N is the number of attributes, and each sub-problem is handled by an independent sub-process during Rare-PEARs execution). Each sub-process starts individually with a different initial population. It then explores the search space of its corresponding sub-problem to find rules with semi-optimal intervals for each of the attributes. This process is done by a new definition of Non-Dominated concept. Rare-PEARs uses this definition to find semi-optimal intervals for attributes during the execution of each sub-process. Finally, Rare-PEARs collects QARs from sub-processes and determines the ultimate Non-Dominated rules based on the interestingness and reliability measures. Rare-PEARs tries to maximize three objectives: interestingness, accuracy and reliability while providing vast coverage on the input dataset. We compared Rare-PEARs with ten algorithms (multi-objective, mono-objective and classical algorithms of association rule mining) over several real-world datasets. The results demonstrate high efficiency of Rare-PEARs.

Feasibility study for banking loan using association rule mining classifier

The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT)–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline