scholarly journals Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Daniele Apiletti ◽  
Eliana Pastor
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Rule Mining ◽  
Electronic Noses ◽  
Data Driven Approach ◽  
The Many ◽  
Coffee Machine

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

scholarly journals Security and privacy concerned association rule mining technique for the accurate frequent pattern identification

2017 ◽  
Vol 7 (1.1) ◽  
pp. 19
Author(s):  
T. Nusrat Jabeen ◽  
M. Chidambaram ◽  
G. Suseendran
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Simulation Environment ◽  
Rule Mining ◽  
Mining Technique ◽  
Diffie Hellman

Security and privacy has emerged to be a serious concern in which the business professional don’t desire to share their classified transaction data. In the earlier work, secured sharing of transaction databases are carried out. The performance of those methods is enhanced further by bringing in Security and Privacy aware Large Database Association Rule Mining (SPLD-ARM) framework. Now the Improved Secured Association Rule Mining (ISARM) is introduced for the horizontal and vertical segmentation of huge database. Then k-Anonymization methods referred to as suppression and generalization based Anonymization method is employed for privacy guarantee. At last, Diffie-Hellman encryption algorithm is presented in order to safeguard the sensitive information and for the storage service provider to work on encrypted information. The Diffie-Hellman algorithm is utilized for increasing the quality of the system on the overall by the generation of the secured keys and thus the actual data is protected more efficiently. Realization of the newly introduced technique is conducted in the java simulation environment that reveals that the newly introduced technique accomplishes privacy in addition to security.

Automatic Item Weight Generation for Pattern Mining and its Application

2013 ◽  
pp. 187-207 ◽  
Author(s):  
Yun Sing Koh ◽  
Russel Pears ◽  
Gillian Dobbie
Keyword(s):  
Uniform Distribution ◽  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Natural Variation ◽  
Pattern Mining ◽  
Weighting Scheme ◽  
Rule Mining ◽  
Real World Application

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

Visualization to Assist the Generation and Exploration of Association Rules

2009 ◽  
pp. 224-245 ◽  
Author(s):  
Claudio Haruo Yamamoto ◽  
Maria Cristina Ferreira de Oliveira ◽  
Solange Oliveira Rezende
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Visual Representations ◽  
Extraction Process ◽  
Rule Mining ◽  
Final Rule ◽  
Rule Sets ◽  
The Many ◽  
Visualization Techniques

Miners face many challenges when dealing with association rule mining tasks, such as defining proper parameters for the algorithm, handling sets of rules so large that exploration becomes difficult and uncomfortable, and understanding complex rules containing many items. In order to tackle these problems, many researchers have been investigating visual representations and information visualization techniques to assist association rule mining. In this chapter, an overview is presented of the many approaches found in literature. First, the authors introduce a classification of the different approaches that rely on visual representations, based on the role played by the visualization technique in the exploration of rule sets. Current approaches typically focus on model viewing, that is visualizing rule content, namely antecedent and consequent in a rule, and/or different interest measure values associated to it. Nonetheless, other approaches do not restrict themselves to aiding exploration of the final rule set, but propose representations to assist miners along the rule extraction process. One such approach is a methodology the authors have been developing that supports visually assisted selective generation of association rules based on identifying clusters of similar itemsets. They introduce this methodology and a quantitative evaluation of it. Then, they present a case study in which it was employed to extract rules from a real and complex dataset. Finally, they identify some trends and issues for further developments in this area.

MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES

2007 ◽  
Vol 21 (04) ◽  
pp. 659-675 ◽  
Author(s):  
YUE XU ◽  
YUEFENG LI
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Community ◽  
Rule Set ◽  
Closed Itemsets ◽  
Minimal Generators

Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.

Real World Association Rule Mining

2002 ◽  
pp. 77-89 ◽  
Author(s):  
Adriano Veloso ◽  
Bruno Rocha ◽  
Márcio de Carvalho ◽  
Wagner Meira
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
World Association

Automatic Item Weight Generation for Pattern Mining and its Application

2011 ◽  
Vol 7 (3) ◽  
pp. 30-49 ◽  
Author(s):  
Yun Sing Koh ◽  
Russel Pears ◽  
Gillian Dobbie
Keyword(s):  
Uniform Distribution ◽  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Natural Variation ◽  
Pattern Mining ◽  
Weighting Scheme ◽  
Rule Mining ◽  
Real World Application

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers

2020 ◽  
Vol 216 ◽  
pp. 109957 ◽  
Author(s):  
Jiangyan Liu ◽  
Daliang Shi ◽  
Guannan Li ◽  
Yi Xie ◽  
Kuining Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Mechanism Analysis ◽  
Action Mechanism ◽  
Rule Mining

Reducing the Feature Space Using Constraint-Governed Association Rule Mining

2017 ◽  
Vol 26 (1) ◽  
pp. 139-152
Author(s):  
◽  
M. Umme Salma
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Medical Data ◽  
Cancer Surveillance ◽  
Feature Subset ◽  
Rule Mining ◽  
Data Set

AbstractRecent advancements in science and technology and advances in the medical field have paved the way for the accumulation of huge amount of medical data in the digital repositories, where they are stored for future endeavors. Mining medical data is the most challenging task as the data are subjected to many social concerns and ethical issues. Moreover, medical data are more illegible as they contain many missing and misleading values and may sometimes be faulty. Thus, pre-processing tasks in medical data mining are of great importance, and the main focus is on feature selection, because the quality of the input determines the quality of the resultant data mining process. This paper provides insight to develop a feature selection process, where a data set subjected to constraint-governed association rule mining and interestingness measures results in a small feature subset capable of producing better classification results. From the results of the experimental study, the feature subset was reduced to more than 50% by applying syntax-governed constraints and dimensionality-governed constraints, and this resulted in a high-quality result. This approach yielded about 98% of classification accuracy for the Breast Cancer Surveillance Consortium (BCSC) data set.

A Multi-Methodological Approach to Rare Association Rule Mining

2010 ◽  
pp. 76-97
Author(s):  
Yun Sing Koh ◽  
Russel Pears
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Methodological Approach ◽  
Processing Technique ◽  
Statistical Techniques ◽  
Rule Base ◽  
Equal Weight ◽  
Rule Mining ◽  
Rare Association

Rare association rule mining has received a great deal of attention in the past few years. In this chapter, the authors propose a multi methodological approach to the problem of rare association rule mining that integrates three different strands of research in this area. Firstly, the authors make use of statistical techniques such as the Fisher test to determine whether itemsets co-occur by chance or not. Secondly, they use clustering as a pre-processing technique to improve the quality of the rare rules generated. Their third strategy is to weigh itemsets to ensure upward closure, thus checking unbounded growth of the rule base. Their results show that clustering isolates heterogeneous segments from each other, thus promoting the discovery of rules which would otherwise remain undiscovered. Likewise, the use of itemset weighting tends to improve rule quality by promoting the generation of rules with rarer itemsets that would otherwise not be possible with a simple weighting scheme that assigns an equal weight to all possible itemsets. The use of clustering enabled us to study in detail an important sub-class of rare rules, which we term absolute rare rules. Absolute rare rules are those are not just rare to the dataset as a whole but are also rare to the cluster from which they are derived.

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