A Systematic Assessment of Numerical Association Rule Mining Methods

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Minakshi Kaushik ◽  
Rahul Sharma ◽  
Sijo Arakkal Peious ◽  
Mahtab Shahin ◽  
Sadok Ben Yahia ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Systematic Assessment ◽  
Mining Methods

An Efficient Compression Technique for Vertical Mining Methods

2007 ◽  
pp. 143-173
Author(s):  
Mafruz Ashrafi ◽  
David Taniar ◽  
Kate Smith
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Main Memory ◽  
Rule Mining ◽  
Compression Technique ◽  
Efficient Manner ◽  
Handling Technique ◽  
Mining Methods ◽  
Mining Algorithms

Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, many of these algorithms require a large amount of main memory to enumerate all frequent itemsets, especially when the dataset is large or the user-specified support is low. Thus, it becomes apparent that we need to have an efficient main memory handling technique, which allows association rule mining algorithms to handle larger datasets in the main memory. To achieve this goal, in this chapter we propose an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner, using bit vectors. Our performance evaluations show that the compression ratio attained by our proposed technique is better than those of the other well-known techniques.

Temporal Event Sequence Rule Mining

2011 ◽  
pp. 1923-1928
Author(s):  
Sherri K. Harms
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Data ◽  
Rule Mining ◽  
Event Sequences ◽  
Use Of Data ◽  
History Of ◽  
Mining Methods ◽  
History Of Use ◽  
Sequence Rule

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. Methods that characterize interesting or unusual patterns from the volumes of temporal data are needed (Roddick & Spiliopoulou, 2002; Han & Kamber, 2005). The association rule mining methods described in this chapter provide the ability to find periodic occurrences of inter-sequential factors of interest, from groups of long, non-transactional temporal event sequences. Association rule mining is well-known to work well for problems related to the recognition of frequent patterns of data (Han & Kamber, 2005). Rules are relatively easy for humans to interpret and have a long history of use in artificial intelligence for representing knowledge learned from data.

scholarly journals Finding Relationship among Various Students’ Attributes with Sport-Interest using Association Rule Mining Method

2018 ◽  
Vol 7 (3) ◽  
pp. 72-75
Author(s):  
Sanjeev Gour
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Study ◽  
Career College ◽  
Rule Mining ◽  
Sport Policy ◽  
Educational Domain ◽  
Educational Institute ◽  
Mining Methods

Data mining methods are widely used in educational domain for the purpose of finding useful information from the large student’s database. This information is then used to understand the behaviors of students in respect of their academic and other curricular performance. One of such Data mining methods called Association rule mining is used in this research study to analyze the student’s database of Career College Bhopal using two mining tools called Weka and XLMiner. The database contain records of 212 students with main attributes like Student’s Gender, Category, Subject, name of district where he/she belong and their parent’s/guardian’s occupation/profession and sport-interest. Sport department of any educational institute also need to understand the behavior and psychology of students for their sport interest to make sport-policy for their institute. In this paper, author has found some unknown relationship among these attributes with respect to sport-interest which is a target attribute. This experimental study has generated many association rules that can be used to answer the questions like which student from particular course, district and category will participate in sports? Which sport usually prefer by male and female student most? Which student can be performed better in which sport? In this way sport policy maker can use these mined information about the sport interest of students to make better decisions in sport framework in an educational institute.

scholarly journals Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 333 ◽  
Author(s):  
Pranomkorn Ampornphan ◽  
Sutep Tongngam
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Patent Data ◽  
Rule Mining ◽  
Trend Forecasting ◽  
Mining Methods ◽  
Patent Documents

A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.

scholarly journals Survey paper on various mining methods on multimedia Images

2013 ◽  
Vol 8 (3) ◽  
pp. 898-901
Author(s):  
Prateek Oswal ◽  
Divakar Singh
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Sequence Mining ◽  
Rule Mining ◽  
Multimedia Document ◽  
Survey Paper ◽  
Mining Sequence ◽  
Recognition Of Objects ◽  
Mining Methods

Multimedia mining is a young but challenging subfield in data mining .Multimedia explanation represents an application of computer vision that presents the recognition of objects or ideas related to a multimedia document as a image. There is not unified conclusion in the concept, content and methods of Multimedia mining, Multimedia mining architecture and framework has to be further studied. there are various mining methods that we can apply on multimedia images like association rule mining, sequence mining, sequence pattern mining etc. In this survey paper we are focusing all this methods. We also discussed feature selection methods of various images.

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