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

Kumar, Pankaj; Sarma, Deva; Mahanta, Anjana Kakati

doi:10.5121/ijdms.2012.4505

Cited by 8 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Last but not least, there is one important point before start applying our post-processing approach is to decide and specify the support threshold value to work with. Generally, there is no single "best" support value for association rule mining, as the optimal value can vary depending on the specific dataset and analysis goals, as suggested by researchers [18][19][20]. On one hand, it can be argued that execution times generally increase inversely with the support values, as shown in [18], where most algorithms have higher execution times for the lowest support value of 0.1 compared to 0.5.…”

Section: Figure 2: Post-processing Approachmentioning

confidence: 99%

See 1 more Smart Citation

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Alawadh,

Barnawi

2024

CMC

View full text Add to dashboard Cite

Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant or redundant rules, uncovering interesting and useful rules, exploring hidden association rules that may affect other factors, and providing backtracking ability for a given product. The proposed approach offers a tailored method that suits specific goals for retailers, enabling them to gain a better understanding of customer behavior based on factual transactions in the target market. We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness. Through this application, we successfully mined a concise set of highly interesting and useful association rules. Out of the 11,265 rules generated, we identified 125 rules that are particularly relevant to the business context. These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

show abstract

Section: Figure 2: Post-processing Approachmentioning

confidence: 99%

“…Distance: This attribute represents the physical distance between the locations of the items involved in the rule [20,21]. In our case, the item distance will be based on the concept of cross-sections, where the distance value between items increases as the items involved in the association rule are from different sections.…”

Section: The Distance Between Itemsmentioning

confidence: 99%

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Alawadh,

Barnawi

2024

CMC

View full text Add to dashboard Cite

show abstract

BruteSuppression: a size reduction method for Apriori rule sets

et al. 2013

View full text Add to dashboard Cite

Association rule mining can provide genuine insight into the data being analysed; however, rule sets can be extremely large, and therefore difficult and timeconsuming for the user to interpret. We propose reducing the size of Apriori rule sets by removing overlapping rules, and compare this approach with two standard methods for reducing rule set size: increasing the minimum confidence parameter, and increasing the minimum antecedent support parameter. We evaluate the rule sets in terms of confidence and coverage, as well as two rule interestingness measures that favour rules with antecedent conditions that are poor individual predictors of the target class, as we assume that these represent potentially interesting rules. We also examine the distribution of the rules graphically, to assess whether particular classes of rules are eliminated. We show that removing overlapping rules substantially reduces rule set size in most cases, and alters the character of a rule set less than if the standard parameters are used to constrain the rule set to the same size. Based on our results, we aim to extend the Apriori algorithm to incorporate the suppression of overlapping rules.

show abstract

Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach

Khan

Parkinson

2018

Expert Systems with Applications

View full text Add to dashboard Cite

Vulnerability assessment and security configuration activities are heavily reliant on expert knowledge. This requirement often results in many systems being left insecure due to a lack of analysis expertise and access to specialist resources. It has long been known that a system's event logs provide historical information depicting potential security breaches, as well as recording configuration activities. However, identifying and utilising knowledge within the event logs is challenging for the non-expert. In this paper, a novel technique is developed to process security event logs of a computer that has been assessed and configured by a security professional, extract key domain knowledge indicative of their expert decision making, and automatically apply learnt knowledge to previously unseen systems by non-experts.The technique converts event log entries into an object-based model and dynamically extracts associative rules. The rules are further improved in terms of quality using a temporal metric to autonomously establish temporal-association rules and acquire a domain model of expert configuration tasks. The acquired domain model and problem instance generated from a previously unseen system can then be used to produce a plan-of-action, which can be exploited by non-professionals to improve their system's security. Empirical analysis is subsequently performed on 20 event logs, where identified plan traces are discussed in terms of accuracy and performance.

show abstract

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

Cited by 8 publications

References 42 publications

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

BruteSuppression: a size reduction method for Apriori rule sets

Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach

Contact Info

Product

Resources

About