Mining Association Rules: A Continuous Incremental Updating Technique

Shan, Shiguang; Wang, Xiaojing; Sui, Miao

doi:10.1109/wism.2010.39

Cited by 8 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. [25] have defined a new data structure called transaction tree. They have also prepared what they call Transaction Sequence Amalgamated (TSA) algorithm to reduce the number of transactions in primitive transactionational database.…”

Section: Incrementally Constructed (I-con) Fp-tree Algorithmmentioning

confidence: 99%

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

Kumar

Soni²

2016

JERA

View full text Add to dashboard Cite

Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

show abstract

Section: Incrementally Constructed (I-con) Fp-tree Algorithmmentioning

confidence: 99%

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

Kumar

Soni²

2016

JERA

View full text Add to dashboard Cite

show abstract

“…However, it is not cost-effective; thus, a number of incremental mining approaches have arisen to address this subject. A significant portion of existing incremental mining research arches focused on the mining of association rules [14]- [16] or sequential patterns [17]- [19] in static incremental data such as traditional transaction databases. According to our surveys, there exist still very few studies have focused on developing a scalable analytical framework for complex event episode mining for cross-disciplinary applications.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications

2022

View full text Add to dashboard Cite

With the ubiquity of sensor networks and smart devices that continuously collect data, we face the challenge of analyzing the growing stream of data in real time. In recent years, there has been a huge need to gain useful knowledge by incrementally analyzing event sequence data. Although episode pattern mining techniques have existed for years, people have recently become more aware of their practical value in solving real-life domain problems such as manufacturing records, stock markets, and weather forecasts. The effective and efficient application of episode pattern mining techniques to analyze complex event data is becoming increasingly important for solving real-life problems in wide domains. However, few studies have focused on developing a scalable framework based on episode pattern mining of complex event sequences for applications in various domains. In this work, we propose a novel framework named SAAF (Scalable Analytical Application Framework) based on complex event episode mining techniques, including batch episode mining, delta episode mining, incremental episode mining, and pattern merging, to consider both efficiency and accuracy. Moreover, to enhance scalability, we adopt the lambda architecture with Apache Spark and Apache Spark Streaming as the system development framework. Finally, the experimental results on three real datasets of different domains and two benchmark datasets showed that the proposed SAAF framework exhibits excellent performance in terms of efficiency, accuracy, and scalability.

show abstract

An Incremental Mining Algorithm for Association Rules Based on Minimal Perfect Hashing and Pruning

Chiou

Tseng

2012

Web Technologies and Applications

View full text Add to dashboard Cite

Mining Association Rules: A Continuous Incremental Updating Technique

Cited by 8 publications

References 14 publications

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications

An Incremental Mining Algorithm for Association Rules Based on Minimal Perfect Hashing and Pruning

Contact Info

Product

Resources

About