Task Of Association Rule Mining Research Articles

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

CRS-iEclat: Implementation of Critical Relative Support in iEclat Model for Rare Pattern Mining

The research purpose is to develop a performance enhancement in Incremental Eclat (iEclat) model by embedding Critical Relative Support (CRS) in mining of infrequent itemset. The CRS measure acts as an interestingness measure (filter) in iEclat model that comprises of i-Eclat-diffset algorithm, i-Eclat-sortdiffset algorithm and i-Eclat-postdiffset algorithm for infrequent (rare) itemset mining. The association rule is performed to reveal the relationships among itemsets in a transactional database. The task of association rule mining is to discover if there exist the frequent itemset or infrequent patterns in the database and if any, an interesting relationship between these frequent or infrequent itemsets can reveal a new pattern analysis for the future decision making. Regardless of frequent or infrequent itemsets, the persisting issues are deemed to execution time to display the rules and the highest memory consumption during mining process. CRS-iEclat engine is proposed to overcome the said issues. Prior to experimentation, results indicate that CRS-iEclat outperforms iEclat from 54% to 100% accuracy on execution time (ET) in selected database as to show the improvement of ET efficiency.

Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment

With the development of big data and cloud computing, data analysis technologies play an important role to produce huge market values. Customers with limited computing resources may resort to the cloud to perform some association rule mining tasks. Data owners may have a risk of personal sensitive information leakage in this process. To preserve privacy in outsourced data, data owners may encrypt raw data before uploading. Data analysis of encrypted data is a challenge that has attracted the attention of many researchers in recent years. Homomorphic encryption is a cryptographic tool, which is one of the ways to solve this challenge. It allows data processing of encrypted data without decryption. Researching homomorphic encryption schemes that support privacy-preserving data mining in a multikey environment has become a significant direction. In this article, we propose a novel homomorphic cryptosystem, which supports multiple cloud users to have different public keys. Besides, we propose a privacy-preserving association rule mining scheme on outsourced data uploaded from multiple parties in a twin-cloud architecture. Our scheme uses a transaction record representation method in databases for large shopping malls based on real-world situations, and our experiments on a real transaction database show that our technology is reasonably feasible.

Mining diversified association rules in big datasets: A cluster/GPU/genetic approach

Association rule mining is a popular data mining task, which has important in many domains. Because the task of association rule mining is very time consuming, evolutionary and swarm based algorithms have been designed to find approximate solutions. However, these approaches still have long execution times, especially when applied on dense and big databases, or when low minsup and minconf threshold values are used. Moreover, these approaches suffer from the lack of diversity in the rules presented to the user. To address these drawbacks of previous algorithms, this paper proposes an efficient parallel algorithm named CGPUGA. It is a genetic algorithm that runs on clusters of GPUs to efficiently discover diversified association rules. It benefits from cluster computing to generate rules. Then, to evaluate rules, which is the most time consuming task, the designed algorithm relies on the massively parallel GPU threads. Furthermore, to deal with the issue of rule quality, the search space of rules is partitioned into several regions assigned to different workers, and rules found by each workers are the merged to ensure diversification. The designed approach has been empirically compared with state-of-the-art algorithms using small, medium, large and big datasets. Results reveal that CGPUGA is 600 times faster than the sequential version of the algorithm for big datasets. Moreover, it outperforms state-of-the-art high performance computing based association rule mining algorithms for real big datasets such as Pokec, Webdocs and Wikilinks. In terms of rule quality, results show that the designed CGPUGA algorithm provides rules of higher quality compared to the state-of-the-art NIGGAR, MSP-MPSO and MPGA algorithms for diversified association rule mining.

Adaptive-Miner: an efficient distributed association rule mining algorithm on Spark

Extraction of valuable data from extensive datasets is a standout amongst the most vital exploration issues. Association rule mining is one of the highly used methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent itemsets) is most crucial part of the association rule mining task. Many single-machine based association rule mining algorithms exist but the massive amount of data available these days is above the capacity of a single machine based algorithm. Therefore, to meet the demands of this ever-growing enormous data, there is a need for distributed association rule mining algorithm which can run on multiple machines. For these types of parallel/distributed applications, MapReduce is one of the best fault-tolerant frameworks. Hadoop is one of the most popular open-source software frameworks with MapReduce based approach for distributed storage and processing of large datasets using standalone clusters built from commodity hardware. But heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce based platforms are being developed for parallel computing in recent years. Among them, a platform, namely, Spark have attracted a lot of attention because of its inbuilt support to distributed computations. Therefore, we implemented a distributed association rule mining algorithm on Spark named as Adaptive-Miner which uses adaptive approach for finding frequent patterns with higher accuracy and efficiency. Adaptive-Miner uses an adaptive strategy based on the partial processing of datasets. Adaptive-Miner makes execution plans before every iteration and goes with the best suitable plan to minimize time and space complexity. Adpative-Miner is a dynamic association rule mining algorithm which change its approach based on the nature of dataset. Therefore, it is different and better than state-of-the-art static association rule mining algorithms. We conduct in-depth experiments to gain insight into the effectiveness, efficiency, and scalability of the Adaptive-Miner algorithm on Spark. Available: https://github.com/sanjaysinghrathi/Adaptive-Miner

An efficient method for discovery of large item sets

In today's emerging field of descriptive data mining, association rule mining (ARM) has been proven helpful to describe essential characteristics of data from large databases. Mining frequent item sets is the fundamental task of ARM. Apriori, the most influential traditional ARM algorithm, adopts iterative search strategy for frequent item set generation. But, multiple scans of database, candidate item set generation and large load of system's I/O are major abuses which degrade the mining performance of it. Therefore, we proposed a new method for mining frequent item sets which overcomes these shortcomings. It judges the importance of occurrence of an item set by counting present and absent count of an individual item. Performance evaluation with Apriori algorithm shows that proposed method is more efficient as it finds fewer items in frequent item set in 50% less time without backtracking. It also reduces system I/O load by scanning the database only once.

Privacy Preserving by Hiding Association Rule Mining from Transaction Database

For making the decision of data mining process some expertise are required, some organization have their own expertise, but many organization doesn't have their own expertise, so the organization helps with some external advisor for the process of data mining. But risk is occurred at the time of getting advice from the external advisor; the question arises regarding the privacy of the customer data and loss of business intelligence. The Security and Privacy of the data are main challenging issues. The owner of the data has some private property like the outsourced database which contains the association rules. However, if the service provider is not trustworthy then integrity of mining results can affect badly. The proposed scheme for privacy preserving mining on databases to protect association rule means the corporate privacy. As per our study, in our paper we are proposing the heuristic based algorithm for hiding the sensitive association rules the algorithm is named as MDSRRC , owner hide sensitive association rule and place transform rules to the server for outsourcing purpose. In this algorithm we are providing an incremental association rule for mining. The recent study concludes that the problem of the incremental association rule mining task's importance was observed, when data is updated. The Matrix Apriori algorithm is proposed which is based on analysis of two association algorithm named as Apriori algorithm and FP-growth algorithm. The matrix Apriori algorithm has a simple structure similar as a matrices and vectors, the algorithm generates frequent patterns and minimizes the number of sets, as compared to previous algorithm. The matrix algorithm is simple and efficient way to generate association rule than the previous algorithm. For hiding the sensitive information of the database proposed algorithm MDSSRC selects the transactions and items by using certain criteria which transform. As per comparing with the previous algorithm the proposed algorithm is much better in performance which can be concluded with the results of the implementation.

Association Rule Mining using Path Systems in Directed Graphs

A transaction database (TDB) consists of a set $I$ of items and a multiset $\mathcal{D}$ of nonempty subsets of $I,$ whose elements are called transactions. There are several algorithms for solving the popular and computationally expensive task of association rule mining from a TDB. In this paper we propose a data structure which consists of a directed graph $D$ (loops and multiple arcs are permitted) and a system of directed paths in $D$ to represent a TDB. We give efficient algorithms for generating the data structure, for extracting frequent patterns and for association rule mining. We also propose several graph theoretic parameters which lead to a better understanding of the system.

Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases

Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-1 other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.

Improving Efficiency of Apriori Algorithm using Cache Database

One of the most popular data mining approach to find frequent itemset in a given transactional dataset is Association rule mining. The important task of Association rule mining is to mine association rules using minimum support value which is specified by the user or can be generated by system itself. In order to calculate minimum support value, every time the complete database has to be scanned for each item in the transaction. This decreases the time complexity of the algorithm. Here we proposed a new algorithm which scan the database once and create a cache database for each transaction using hash map. This cache copy is then used to search for frequent item sets. Due to which the overhead of scaning complete database for each item is reduced, and efficiency is increased. Key word: Apriori, cache database, hash map, scanning time, time complexity.

Reduction of Number of Association Rules with Inter Itemset Distance in Transaction Databases

Association Rule discovery has been an important problem of investigation in knowledge discovery and data mining. An association rule describes associations among the sets of items which occur together in transactions of databases.The Association Rule mining task consists of finding the frequent itemsets and the rules in the form of conditional implications with respect to some prespecified threshold values of support and confidence.The interestingness of Association Rules are determined by these two measures. However, other measures of interestingness like lift and conviction are also used. But, there occurs an explosive growth of discovered association rules and many of such rules are insignificant. In this paper we introduce a new measure of interestingness called Inter Itemset Distance or Spread and implemented this notion based on the approaches of the apriori algorithm with a view to reduce the number of discovered Association Rules in a meaningful manner. An analysis of the working of the new algorithm is done and the results are presented and compared with the results of conventional apriori algorithm.

Predicting Missing Items in Shopping Cart using Associative Classification Mining

The primary task of association rule mining is to detect frequently co-occurring groups of items in transactional databases. The intention is to use this knowledge for prediction purposes. So many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in and less attention has been paid to the methods that exploit these for prediction purposes. This paper contributes to the latter task by proposing a technique that uses the partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy, for example, If bread, butter, and milk often appear in the same item, then the presence of butter and milk in a shopping cart suggests that the customer may also buy bread. More generally knowing which items a shopping cart contains, we want to predict often items that the customer is likely to add before proceeding to the checkouts. So this paper presents a technique called the Matrix whose principal diagonal elements represent the association among items and looking to the principal diagonal elements, the customer can select what else the other items can be purchased with the currently contents of the shopping cart and also reduces the rule mining cost. The association among items is shown through Graph. The frequent itemsets are generated from the Combo Matrix. Then association rules are to be generated from the already generated frequent itemsets. The association rules generated form the basis for prediction. The incoming itemsets i.e. the contents of the shopping cart will be represented by set of unique indexed numbers and the association among items is generated through the Combo Matrix. Finally the predicted items are suggested to the Customer.

A Dynamic Web Mining Framework for E-Learning Recommendations using Rough Sets and Association Rule Mining

wide web is a huge information source, broadly used for learning now-a-days due to flexibility of time, sharing of learning resources and infrastructure etc., Most of web based learning system lacks expert-learner interaction, assessment of user activities and learners are getting drowned by huge number of web pages in the learning web site and they find difficulties in choosing suitable materials relevant to their interest. This work attempts to engage e-learners at an early stage of learning by providing navigation recommendations. E-learning personalization is done by mining the web usage data like recent browsing histories of learners of similar interest. The proposed method uses upper approximation based rough set clustering and dynamic all k th order association rule mining using apriori for personalizing e-learners by providing learning shortcuts. The essence of combing association rule and clustering is that, using clustered access patterns can reduce the data set size for association rule mining task, and improves the recommendation accuracy.

Application-independent feature construction based on almost-closedness properties

Feature construction has been studied extensively, including for 0/1 data samples. Given the recent breakthroughs in closedness-related constraint-based mining, we are considering its impact on feature construction for classification tasks. We investigate the use of condensed representations of frequent itemsets based on closedness properties as new features. These itemset types have been proposed to avoid set counting in difficult association rule mining tasks, i.e. when data are noisy and/or highly correlated. However, our guess is that their intrinsic properties (say the maximality for the closed itemsets and the minimality for the δ-free itemsets) should have an impact on feature quality. Understanding this remains fairly open, and we discuss these issues thanks to itemset properties on the one hand and an experimental validation on various data sets (possibly noisy) on the other hand.

Support-Less Association Rule Mining Using Tuple Count Cube

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

Discovering frequent itemsets by support approximation and itemset clustering

To speed up the task of association rule mining, a novel concept based on support approximation has been previously proposed for generating frequent itemsets. However, the mining technique utilized by this concept may incur unstable accuracy due to approximation error. To overcome this drawback, in this paper we combine a new clustering method with support approximation, and propose a mining method, namely CAC, to discover frequent itemsets based on the Principle of Inclusion and Exclusion. The clustering technique groups highly similar members to improve the accuracy of support approximation. The hit ratio analysis and experimental results presented in this paper verify that CAC improves accuracy. Without repeatedly scanning a database and storing vast information in memory, the CAC method is able mine frequent itemsets with relative stability. The advantages that the CAC method enjoys in both accuracy and performance make it an effective and useful technique for discovering frequent itemsets in a database.

CoMMA: a framework for integrated multimedia mining using multi-relational associations

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. On the basis of this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multi-relational association rule mining where the relations exist between image-based features, and textual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images.In this paper we also present a multi-relational extension to the FP-tree algorithm to accomplish the association rule mining task effectively. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our auto-annotation experiments on different test sets. Another contribution of this paper is highlighting a need to develop robust evaluation metrics for the image annotation task. We propose several applicable scoring techniques and then evaluate the performance of the different algorithms to study the utility of these techniques. A detailed analysis of the datasets used and the performance results are presented to conclude the paper.