EStream: Online Mining of Frequent Sets with Precise Error Guarantee

2006 ◽  
pp. 312-321
Author(s):  
Xuan Hong Dang ◽  
Wee-Keong Ng ◽  
Kok-Leong Ong
Keyword(s):  
Frequent Sets ◽  
Online Mining

Online mining of frequent sets in data streams with error guarantee

2007 ◽  
Vol 16 (2) ◽  
pp. 245-258 ◽  
Author(s):  
Xuan Hong Dang ◽  
Wee-Keong Ng ◽  
Kok-Leong Ong
Keyword(s):  
Data Streams ◽  
Frequent Sets ◽  
Online Mining

Interactive Visual Analytics of Databases and Frequent Sets

2013 ◽  
Vol 3 (4) ◽  
pp. 120-140 ◽  
Author(s):  
Carson K.S. Leung ◽  
Christopher L. Carmichael ◽  
Patrick Johnstone ◽  
David Sonny Hung-Cheung Yuen
Keyword(s):  
Visual Analytics ◽  
Journal Article ◽  
Real Life ◽  
Visual Representations ◽  
Analytic Solutions ◽  
Useful Knowledge ◽  
Frequent Sets ◽  
Large Databases ◽  
Advanced Analysis

In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. Many of the existing visualizers were not designed to visualize these mined frequent sets. In this journal article, an interactive visual analytic system is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the management, visualization, and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.

MOOC opinion mining based on attention alignment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuanxin Ouyang ◽  
Hongbo Zhang ◽  
Wenge Rong ◽  
Xiang Li ◽  
Zhang Xiong
Keyword(s):  
Opinion Mining ◽  
Language Models ◽  
Online Course ◽  
Data Sets ◽  
Alignment Method ◽  
Dependency Analysis ◽  
Content Type ◽  
Massive Open Online Course ◽  
Frequent Sets ◽  
Alignment Mechanism

Purpose The purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments. Design/methodology/approach The approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism. Findings The experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets. Originality/value To take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.

Efficient online mining of large databases

2007 ◽  
Vol 2 (3) ◽  
pp. 328
Author(s):  
Fadila Bentayeb ◽  
Jerome Darmont ◽  
Cecile Favre ◽  
Cedric Udrea
Keyword(s):  
Large Databases ◽  
Online Mining

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

An efficient and flexible algorithm for online mining of large itemsets

2004 ◽  
Vol 92 (6) ◽  
pp. 311-316 ◽  
Author(s):  
Kuen-Fang Jea ◽  
Ming-Yuan Chang ◽  
Ke-Chung Lin
Keyword(s):  
Online Mining
Sign in / Sign up

Export Citation Format

Share Document