Traditional Association Rule Mining Algorithms Research Articles

COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining

Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.

Fast Top-K association rule mining using rule generation property pruning

Traditional association rule mining algorithms can have a long runtime, high memory consumption, and generate a huge number of rules. Browsing through numerous rules and adjusting parameters to find just enough rules is a tedious task for users, who are often only interested in finding the strongest rules. Hence, many recent studies have focused on mining the top-k most frequent association rules that have a minimum confidence so as to limit the number of rules by ranking them by frequency. Though this redefined task has many applications, the performance of current algorithms remains an issue. To address this issue, this paper presents a novel algorithm named FTARM (Fast Top-K Association Rule Miner) to efficiently find the set of top-k association rules using a novel technique called Rule Generation Property Pruning (RGPP). This technique reduces the search space by analyzing the internal relationships between items of the database to be mined and the parameters set by users. Furthermore, a novel candidate pruning property is used by this technique to speed up the mining process. FTARM’s efficiency was evaluated on various public benchmark datasets. A substantial reduction of the association rule mining time and memory usage was observed, and that FTARM has good scalability, which can benefit to many applications.

Determining profitable products in the retail market with consideration of cash limitation and exhibition periods

In the selection of profitable products, consumer preferences and retailer constraints in products supply must be considered. When data mining algorithms are used to discover the consumer's preferences from transaction database, the results may be biased, if the exhibition period of the products has not be considered. In this study a new method is proposed to adjust the support and confidence coefficients of traditional association rule mining algorithms such as Apriori or FP-growth taking into consideration of common exhibition periods. On the supply side, the retailer may have some limitations in terms of buying and supplying products in the store. In the most recent researches, only the shelf space constraint has been considered. In this study, financing as an important constraint in the retail market and the opportunity cost of money are imported in the selection of profitable products.The researcher's experiment on real world data shows that the number of frequent itemsets increases significantly when products exhibition periods are taken into consideration. In this case, if the retailer considers the opportunity cost of money as 1%, the profitable set composition will be changed by 10%. Also, when the opportunity cost is 1% and the retailer faces cash limitation, the number of products is reduced by 21% in the most profitable set, whereas the new set composition is 29.4% different from the base set.

A lattice-based approach for mining high utility association rules

Most businesses focus on the profits. For example, supermarkets often analyze sale activities to investigate which products bring the most revenue, as well as find out customer trends based on their carts. To achieve this, a number of studies have examined high utility itemsets (HUI). Traditional association rule mining algorithms only generate a set of highly frequent rules, but these rules do not provide useful answers for what the high utility association rules are. Therefore, Sahoo et al. (2015) proposed an approach to generate utility-based non-redundant high utility association rules and a method for reconstructing all high utility association rules. This approach includes three phases: (1) mining high utility closed itemsets (HUCI) and generators; (2) generating high utility generic basic (HGB) association rules; and (3) mining all high utility association rules based on HGB. The third phase of this approach consumes more time when the HGB list is large and each rule in HGB has many items in both antecedent and consequent. To overcome this limitation, in this paper, we propose an algorithm for mining high utility association rules using a lattice. Our approach has two phases: (1) building a high utility itemsets lattice (HUIL) from a set of high utility itemsets; and (2) mining all high utility association rules (HARs) from the HUIL. The experimental results show that mining HARs using HUIL is more efficient than mining HARs from HGB (which is generated from HUCI and generators) in terms of runtime and memory usage.

Sequential Association Rules Based on Apriori Algorithm Applied in Personal Recommendation

The association rules is a data mining technology that is to find the regularity from the massive data. Based on the imperfection of the traditional association rule mining algorithm ,and in consider of the electronic commerce time latitude, this text proposed temporal association rules algorithm, which is also applied to the field of electronic commerce. Through analyzing the record of consumer of some category customers purchased during the last month from a website, dig out the regularity of customers choosing their products, to help sellers recommend products the users may be interested in, and developing a reasonable marketing strategy.

Multi-objective association rule mining with binary bat algorithm

Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algorithms such as genetic algorithm is slow and the efficiency of these algorithms is low. Furthermore, the rules generated by traditional multi-objective algorithms are too large to be efficiently analyzed and explored in any further process. Bat algorithm is a new efficient global optimal algorithm whose convergence is superior to binary particle swarm optimization (BPSO) and genetic algorithm. This paper discusses the application of multi-objective bat algorithm to association rule mining. We propose multi-objective binary bat algorithm (MBBA) based on Pareto for association rule mining. This algorithm is independent of minimum support and minimum confidence. To evaluate the association rules mined by MBBA algorithm, we propose a new method to discover interesting association rules without favoring or excluding any measure. Compared with the single-objective BPSO, binary bat algorithm (BBA) and Apriori algorithm, the experimental results on six datasets show that the new algorithm is feasible and highly effective. It can make up the shortage of single objective algorithms and traditional association rule mining algorithms.

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.

Optimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce

Data mining defines hidden pattern in data sets and association between the patterns. In data mining, association rule mining is key technique for discovering useful patterns from large collection of data. Frequent itemset mining is a famous step of association rule mining. Frequent itemset mining is used to gather item sets after discovering association rules. Some limitations exist with the traditional association rule mining algorithms for large-scale data. As for FP-Growth algorithm, the success is limited by internal memory size because mining process is on the base of large tree-form data structure. A new traditional approach, FPgrowth technique is very efficient in large amount of data. FPGrowth algorithm constructs conditional frequent pattern tree and conditional pattern based from database which satisfies the minimum support. However, FP growth algorithm requires a tree storage structure, which results in high computation time. The proposed algorithm realizes to construct Optimum pattern Tree with the node as the data item of the transaction. This rare algorithm is implemented on Hadoop to reduce the computation cost. The Hadoop environment supports for handling the large data and process them in parallel manner for better performance. The optimal frequent pattern is obtained that satisfies the minimum support and confidence value.

Infrequent Weighted Item Set Mining in Complex Data Analysis

Infrequent Weighted Association Mining (IWAM) is one of the main areas in data mining for extracting the rare items in high dimensional datasets. Traditional Association rule mining algorithms produce large number of candidate sets along with the database scans. Due to large number of transactions and database size, traditional methods consume more time to find the relevant association rules with the specified threshold. Prior and post database scans are required an additional effort to validate the association rules. Most of the existing weighted models are implemented for mining frequent itemsets, but finding infrequent itemset mining are useful in many recent fields like web,medical,cloud,complex databases,protein sequence etc. In weighted infrequent association rule mining, each item in the transaction is assigned a weight in order to mine high utility infrequent itemsets. In this proposed work, weighted association rule mining algorithm is proposed to find infrequent itemsets using weighted threshold measures. Proposed approach gives better results on real-time datasets compare to existing weighted models.

Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals

In traditional association rule mining algorithms, if the minimum support is set too high, many valuable rules will be lost. However, if the value is set too low, then numerous trivial rules will be generated. To overcome the difficulty of setting minimum support values, global and local patterns are mined herein. Owing to the temporal factor in association rule mining, an itemset may not occur frequently in the entire dataset (meaning that it is not a global p attern), but it may appear frequently over specific intervals (meaning that it is a local pattern). This paper proposed a temporal ass ociation rule mining algorithm for interval frequent-patterns, called GLFMiner, which automatically and efficiently generates all intervals w ithout prior domain knowledge in an efficient manner. GLFMiner considers not only global frequent-patterns, but also local frequent-patterns. Using the same value of minimum support, it can locate many valuable temporal rules without losing the rules that traditio nal algorithms may find. Experimental results reveal that our novel algorithm mines more temporal frequent-patterns than traditional association rule mining algorithms and is effective in real-world applications such as market basket analysis and intrusion detection systems.

Principal Association Mining: An efficient classification approach

Classification is one of the key tasks in business intelligence, decision science, and machine learning. Associative classification has aroused significant research interest in recent years due to its superior accuracy. Traditional association rule mining algorithms often yield many redundant and sometimes conflicting class association rules. This paper presents a new, efficient associative classification approach. This new approach produces a compact classifier with a small number of association rules, yet with good classification performance. This approach is based on a novel rule quality metric, named as Principality, which measures an association rule’s classification accuracy and coverage for a specific class. Heuristic methods utilizing the Principality metric are applied to rule pruning and associative classifier construction to produce a compact classifier. This Principal Association Mining (PAM) approach is confirmed to be effective at improving classification accuracy as well as decreasing classifier size by experiments conducted on 17 datasets.

Association Rule Generation using Modified Hashing Function

rule mining is one of the most interesting and challenging task in data mining process. There exists many association rule mining techniques, each having merits and demerits. The main problem that exists in many traditional association rule mining algorithms is that these algorithms need more than one database scan to generate association rules. As scanning the database is a costly operation, algorithms capable of generating association rules with only one scan is the need of the hour. In this paper, a novel algorithm for generating association rules is presented which uses hashing function. This algorithm scans the database only once by utilizing the latest version of priori algorithm, direct hashing algorithm and pruning process. The algorithm discovers set of association rules from frequent k-item sets by computing the frequency of each item set. Then pruning process is applied to minimize the number of item sets generated after scanning the size of the database. Experimental results show that our method is very effective in generating association rules without any collision, leading to very high data accuracy. KeywordsRule Mining, ARM, Hashing, Pruning, Basket

Mining useful association rules from questionnaire data

Traditional association rule mining algorithms often have difficulty handling questions that are implicitly related, producing rules that are very accurate but are so obvious as to be completely useless to researchers. This problem is compounded by the fact that standard objective measures for interestingness often capture the wrong information and are maximized by these obvious rules. We propose an enhancement to standard association rule mining that uses clustering to identify related questions to pre-prune rules involving similar questions, which are less likely to be subjectively interesting. This enhancement reduces the search space of rules, improving the algorithm's efficiency. In addition, the resulting rule list has a higher concentration of diverse rules, more likely to be useful to researchers. We demonstrate this improvement relative to existing algorithms on two real-world, public health questionnaires.

Web usage mining with evolutionary extraction of temporal fuzzy association rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets’ boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules.

Study on Network Security Audit System Based on Agent Technology and Log Mining

Aiming to increase network security auditing efficiency, an audit system with good efficiency has been generated through Agent and log mining technology. This paper improves the traditional association rule mining algorithm Apriori and designs a new data structure in which each brother node is in a parallel alignment, each child node has a pointer directing to their children node and sibling node, the audit system can only scan the database once. Experiments show that compared with traditional Apriori, the improved algorithm has greatly reduced the I/O expenditure of the system, having obvious superiority.

Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining

Collaborative Filtering (CF) is the most prevalent technique in recommender systems and facilitates the high-quality recommendations by identifying similar users based on their logged history of prior transactions. However, accuracy of recommendations and sparsity are still major concerns related to CF recommendation techniques. Recent research in CF is investigating the use of Association Rule Mining (ARM) for extracting high-level information and thereby providing more accurate recommendations. However, determination of the threshold values for support and confidence measures affect the quality of association rules. Moreover, the traditional ARM algorithms are based on market basket analysis and therefore degrade computation efficiency by mining too many association rules which are not appropriate for a given user. The proposed approach attempts to improve the quality of recommendations through the application of Multi-objective Particle Swarm Optimization (MOPSO) algorithm for ARM in the framework of CF. Consequently, by considering support and confidence measures as different objectives, the MOPSO based ARM model extracts only useful and eminent direct association rules which are optimal in the wider sense that no other rules are superior to them when both objectives are simultaneously considered. In addition, computational efficiency is enhanced by mining rules only for the given user and over the related transactional database. Further, the present work explores the indirect (transitive) association between users as well as between items for providing more accurate recommendations even with highly sparse history of transactions. In order to evaluate the effectiveness of our approach, we conducted an experimental study using the MovieLens data set. Experimental results clearly reveal that the proposed method consistently outperform other traditional CF based methods as measured by recommendation accuracy, precision, and recall.