scholarly journals The Bases of Association Rules of High Confidence

2018 ◽  
Author(s):  
Oren Segal ◽  
Justin Cabot-Miller ◽  
Kira Adaricheva ◽  
Nation J.B ◽  
Anuar Sharafudinov
Keyword(s):  
Association Rules ◽  
High Confidence

Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse

2008 ◽  
pp. 3222-3234
Author(s):  
Yun Sing Koh ◽  
Nathan Rountree ◽  
Richard O’Keefe
Keyword(s):  
Data Mining ◽  
Rare Disease ◽  
Association Rules ◽  
Frequent Itemsets ◽  
New Method ◽  
Apriori Algorithm ◽  
High Confidence ◽  
Minimum Support ◽  
Rare Association ◽  
Support Threshold

Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. To alleviate this problem, we propose a new method of discovering sporadic rules without having to produce all other rules above the minimum support threshold. The new method, called Apriori-Inverse, is a variation of the Apriori algorithm that uses the notion of maximum support instead of minimum support to generate candidate itemsets. Candidate itemsets of interest to us fall below a maximum support value but above a minimum absolute support value. Rules above maximum support are considered frequent rules, which are of no interest to us, whereas rules that occur by chance fall below the minimum absolute support value. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). This article is an expanded version of Koh and Rountree (2005).

Strong Symmetric Association Rules and Interestingness Measures

2010 ◽  
pp. 185-203
Author(s):  
Agathe Merceron
Keyword(s):  
Data Mining ◽  
Case Studies ◽  
Association Rules ◽  
Strong Support ◽  
Application Domain ◽  
High Confidence ◽  
Special Shape ◽  
Chi Square ◽  
Interestingness Measures ◽  
Domain Experts

Strong symmetric association rules are defined as follows. Strong means that the association rule has a strong support and a strong confidence, well above the minimum thresholds. Symmetric means that X?Y and Y?X are both association rules. Common objective interestingness measures such as lift, correlation, conviction or Chi-square tend to rate this kind of rule poorly. By contrast, cosine is high for such rules. However, depending on the application domain, these rules may be interesting regarding criteria such as unexpectedness or actionability. In this chapter, the authors investigate why the abovementioned measures, except cosine, rate strong symmetric association rules poorly, and show that the underlying data might take a quite special shape. This kind of rule can be qualified as rare, as they would be pruned by many objective interestingness measures. Then the authors present lift and cosine in depth, giving their intuitive meaning, their definition and typical values. Because lift has its roots in probability and cosine in geometry, these two interestingness measures give different information on the rules they rate. Furthermore they are fairly easy to interpret by domain experts, who are not necessarily data mining experts. They round off our investigation with a discussion on contrast rules and show that strong symmetric association rules give a hint to mine further rare rules, rare in the sense of a low support but a high confidence. Finally they present case studies from the field of education and discuss challenges.

scholarly journals Sistem Rekomendasi Buku untuk Perpustakaan Perguruan Tinggi Berbasis Association Rule

2019 ◽  
Vol 3 (2) ◽  
pp. 304-312
Author(s):  
Laras Dewi Adistia ◽  
Tubagus Mohammad Akhriza ◽  
Singgih Jatmiko
Keyword(s):  
Information System ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Web Applications ◽  
Rule Mining ◽  
High Confidence ◽  
Transaction Data ◽  
The University

One of the services in the university library is an information system to find the availability of library collections and the location of each collection shelf. But not many of these systems provide a mechanism that can recommend visitors not only about the books they want, but also other related books that may be needed. This study uses association rule mining techniques that are applied to library transaction data to identify relationships between books (titles) that attract visitors' attention. Relationships are built on interesting measurements between the titles, namely support and confidence, where support determines the combination of the most frequently borrowed book titles, while confidence produces the possibility that the title of the book will be borrowed along with other books. The pattern of book titles association with high confidence indicates that the titles are very related so it is recommended for visitors to consider borrowing along with the book they are looking for. In addition, the system can also recommend the procurement of new books and rack configurations to improve the visitor's experience when searching for books on the site. In the experiment, the precision of recommendations generated from the system reached 70%. Web applications were developed to help understand the effectiveness of the recommendation system based on association rules.

The role of recollection in high-confidence know judgments

2010 ◽  
Author(s):  
Laura Mickes ◽  
Vivian Hwe ◽  
John T. Wixted
Keyword(s):  
High Confidence
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