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.

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.

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 Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

2022 ◽  
Vol 1 ◽  
Author(s):  
Agostinetto Giulia ◽  
Sandionigi Anna ◽  
Bruno Antonia ◽  
Pescini Dario ◽  
Casiraghi Maurizio
Keyword(s):  
Machine Learning ◽  
16S Rrna ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Microbial Community Composition ◽  
Frequent Itemset ◽  
Supervised Machine Learning ◽  
Rule Mining ◽  
Microbiome Data

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity

2021 ◽  
pp. 241-253
Author(s):  
Alexandar Vincent-Paulraj ◽  
Girvan Burnside ◽  
Frans Coenen ◽  
Munir Pirmohamed ◽  
Lauren Walker
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Sequential Association Rule Mining

Optimal Inventory Classification using Data Mining Techniques

2016 ◽  
pp. 236-255 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Consumer Behavior Analysis ◽  
Relationship Extraction ◽  
Using Data ◽  
Research Studies

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

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

scholarly journals Study to Determine Adverse Diseases Pattern using Rare Association Rule Mining

2020 ◽  
pp. 519-526
Author(s):  
Keerti Shrivastava ◽  
Varsha Jotwani
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Security Applications ◽  
Rare Association ◽  
The Common ◽  
Set Up ◽  
Original Motivation

Data mining is a method for finding patterns from repositories that remain hidden, unknown but fascinating. It has resulted in a number of strategies and emphasizes the detection of patterns to identify patterns that occur frequently, seldom and rarely. With their implementations, the work has improved the efficiency of the techniques. Yet typical methods for data mining are limited to databases with static behavior. The first move was to investigate similarities between the common objects through association rules mining. The original motivation for the search for these guidelines was the consumers ' shopping patterns in transaction data for supermarkets. This attempts to classify combinations of items or items that influence the presence likelihood of other items or items in a transaction. The request for rare association rule mining has improved in current years. The identification of unusual data patterns is critical, including medical, financial, or security applications. This survey seeks to give an analysis of rare pattern mining strategies, which in general, comprehensive and constructed. We discuss the issues in the quest for unusual rules using conventional association principles. Because mining rules for rare associations are not well known, special foundations still need to be set up.

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